ROCK VOLATILE GEOLOGIC LIQUID PRESSURE QUANTIFICATION METHODS AND RELATED TECHNOLOGY

Description

CROSS REFERENCE TO RELATED APPLICATIONS/PRIORITY

This patent application claims the benefit of priority to U.S. Provisional Patent Application 63/704,491, filed Oct. 7, 2024, Entitled ROCK VOLATILE GEOLOGIC LIQUID PRESSURE QUANTIFICATION METHODS AND RELATED TECHNOLOGY. This application incorporates by reference the entirety of the above-referenced/related priority application.

TECHNICAL FIELD

The technology described here relates to the methods of using rock volatile compound(s) to characterize the state of geologic fluid resources or geologic materials/units.

BACKGROUND

“Geologic resources” are generally understood to mean commercially valuable geologic materials (e.g., rocks, soils, and subterranean fluids). Geologic materials can be characterized as, e.g., metallic resources (e.g., gold, lithium), nonmetallic rock and rock-derivative resources (e.g., diamonds or potash fertilizers), and energy resources (e.g., uranium or other radioactive materials). Geologic resources can be composed of solid materials, fluids (liquids or gases), or can be composed of mixtures thereof, or can comprise mixtures thereof. Petroleum and related hydrocarbon energy resources, for example, generally are or comprise (e.g., mostly comprise) fluid geologic resources (natural gas, crude oil, etc.). Geologic resources can be further characterized on the basis of such compositional elements.

For example, the term “petroleum resources” has been used in the art to refer to “the remaining recoverable hydrocarbons within the Earth” (petroleum itself is defined as “naturally occurring hydrocarbon in any phase”). See Tarek Ahmed, D. Nathan Meehan, ADVANCED RESERVOIR MANAGEMENT AND ENGINEERING (Second Edition), Gulf Professional Publishing, 2012, Pages 587-649, ISBN 9780123855480. In some cases, the term “reserve” is used to differentiate from resources, generally, with reserves often meaning hydrocarbon resources that are discovered, recoverable, and remaining at levels of commercial value. Id. The condition of a geologic resource may be referred to as its “state.”

Understanding geologic resource/unit state or resource characteristics (e.g., the state of formation fluids such as petroleum reservoirs) is important to subterranean resource exploration or production/extraction (e.g., petroleum production) and other subterranean activities (e.g., carbon sequestration). Quantifiable states/characteristics are particularly important or beneficial to commercial geologic resource utilization (extraction or other use, such as storage of other materials). Quantification measures in technology can be characterized as direct measures or indirect measures. Outside of individual counting/weighing methods or dimension measurements, most quantification measures in geology and other natural sciences involve some amount of correlation between a measured event/object and the associated property that the event/object is purported to measure (e.g., movement of mercury in a traditional thermometer or barometer as a measure of temperature or atmospheric pressure, respectively). Such reliable correlations are still considered quantitative “measures” in the art, even where the measure comes with a less-than-perfect degree of reliability or accuracy, so long as the measure is sufficiently reliable for its intended purpose.

Quantifiable geologic resource states can include, e.g., pressure, permeability, and resource-associated material chemistry. Consideration of pressure in a geologic area, such as a borehole environment, can be particularly important to help direct whether a geologic unit (a site, area, play, reservoir, etc.) warrants development for production or geologic substance storage purpose(s). Reservoir pressure, for example, is known to be an “important factor governing the phase behavior and the properties of the reservoir fluids.” See Abdus Satter, Ghulam M. Iqbal, RESERVOIR ENGINEERING, Gulf Professional Publishing, 2016, Pages 81-105, ISBN 9780128002193. Changes in pressure can also be important in understanding the nature of a geologic unit. As a petroleum reservoir is produced and depleted, any one or more of the following can occur: dissolution or liberation of gas phase from oil phase in the reservoir, changes in oil volume (as oil is slightly compressible and more predominant due to the liberation of the gas phase), expansion of gas, or retrograde condensation (where the formation of oil droplets occurs from the gas phase). Moreover, “as Darcy's law suggests, the oil and gas production rates depend upon the reservoir pressure and the pressure at the wellbore.” Id.

Pressure in subterranean environments, such as in formation fluids associated with a borehole/wellbore, can sometimes be measured using pressure-measuring technologies. The art often separates measured pressure vs. estimated pressure methods. Estimated pressure determining methods include calculating hydrostatic pressures from measured water density or salinity, estimating hydrostatic pressures from fluid density using Rw (formation water resistivity), using the weight of drilling mud, or using the rule-of-thumb pressure gradient, 0.465 psi/ft. Many of these estimation methods are based on indirect/comparative measurements. The ability of such indirect/comparative measurements to accurately assess actual fluid resource pressure can vary widely based on technique, environment/conditions, etc. Moreover, in some cases, some of these techniques are generally unreliable or unsuitable (e.g., there are limitations in when Rw measurements can be made). Direct pressure measurements can be performed by a smaller number of methods, including repeat format tester (RFT) methods (sometimes called MDT methods), drill stem testing (DST) methods (sometimes called full production testing), and reservoir bottom-hole pressure buildup tests.

DST is often considered the “gold standard” for such pressure testing and has a long history of use in the art. See, e.g., W. Marshall Black, “A Review of Drill-Stem Testing Techniques and Analysis,” J Pet Technol 8 (06): 21-30. Jun. 1, 1956. Paper Number: SPE-589-G doi.org/10.2118/589-G; Bredehoeft, J. D. (1965), The Drill-Stem Test: The Petroleum Industry's Deep-Well Pumping Test. Groundwater, 3: 31-36. doi.org/10.1111/j.1745-6584.1965.tb01218.x; and, e.g., Mohammed Khalid et al., June 2020, Drill Stem Test Applications and Data Interpretation By Using Ecrin Saphir Software (A Kurdistan Well as a case study) (available at researchgate.net/publication/271826746_Drill-Stem_Testing_DST). However, DST can be quite expensive, and in many cases DST pressure measurements may be unreliable (such as, e.g., in unconventional borehole environments) or completely unavailable (such as, e.g., in horizontal borehole environments). To perform DST, drilling operations usually must be temporarily halted to allow the pressure measurement device to be lowered into the hole where a pressure measurement is desired. Depending on the nature of the rock, such as in unconventional tight formations, it may take hours of delays in drilling to obtain a pressure measurement, and even once obtained, the measurement may be unreliable if an equilibrium pressure is not obtained. Further, due to the nature of how DSTs are performed (that is, e.g., by lowering the equipment into the hole by gravity), DSTs are unsuitable for horizontally-drilled wells, where the force of gravity cannot advance the DST instruments.

WO2020051259 and related US continuation-in-part application Ser. No. 17/194,194 (published as US 2021-0215652) (the '194 publication) describe inventive technology involving the use of rock volatile analysis techniques (initially described in WO 2018/111945) and other methods to evaluate, among other things, the carbonate grain character, e.g., coarseness, of rock(s), including petroleum drill cutting samples. Such described methods typically include causing the release of carbon dioxide from such rock materials and analyzing such compounds to evaluate the rock properties of samples. The '194 publication also describes the use of rock volatile stratigraphy (RVS) technology to measure rock volatile carbon dioxide from different locations and using such comparative measurements to identify faults and other pressure loss/fluid conduits (which are described as “features that result in detectable or significant pressure loss”). The '194 publication specifically applies these and other methods to petroleum production. The '194 publication also describes practicing such methods with “carbon dioxide-related compounds” or “CDRCs,” which are described as compounds that (a) are chemically similar to carbon dioxide and found naturally within fluid inclusions and/or (b) are converted readily to CO₂ under the conditions that promote release of the compounds from the rock samples and/or capture of such volatile compounds for analysis. The '194 publication states that the most common CDRCs include carbonic acid (H₂CO₃) and bicarbonate (HCO₃⁻), but indicates CDRCs can also include carboxylic organic acids such as formic and/or acetic acid.

US20230081834 A1 (the '834 publication) somewhat expands on the work described in the '194 publication, reporting that carbon dioxide obtained from rock volatiles can be associated with “a reduction in formation pressure associated with oil and gas production from oil and gas wells” or “from natural reservoir seal failure, that is, caused by disruptive features (such as faults).” The '834 publication further describes the analysis of “events or conditions that can cause changes over time” that can be evaluated through such methods, which “can include, e.g., changes in pressure in a site (e.g., due to removal of material, such as removal of petroleum from the site, which may result in a rupture of a formation seal or barrier), changes in material, seismic events, developments of faults or other structural changes, or chemical changes in the composition of a material in the formation or site rocks.” The '834 publication specifically exemplifies such methods, comparing rock volatiles carbon dioxide analysis of well cuttings from 2019 to cuttings from a well drilled in 1995. The '834 publication further describes the use of such methods to assess sites for carbon sequestration.

The methods of the '194 publication and the '834 publication provided valuable new ways to identify faults and other pressure loss-associated features in geologic areas, and to evaluate changes in geologic resources associated with pressure loss events through rock volatiles carbon dioxide evaluation. While such methods have undoubtedly reflected substantial breakthroughs in petroleum exploration and production and carbon sequestration methods, the methods provided by these publications are relative/comparative analytical methods and, like other fluid resource state evaluation methods, do not provide a basis for reliable, consistent quantitation of geologic unit pressure.

SUMMARY

This document includes a section entitled “CONSTRUCTION PRINCIPLES AND DESCRIPTION OF SELECT TERMS” that readers are encouraged to consult to help properly interpret the disclosure provided in this section and elsewhere. That section includes a list of acronyms frequently used in this disclosure.

This Summary section briefly describes elements and characteristics of selected illustrative embodiment(s) of the technology of this disclosure. The brief summaries of such embodiments provided here are primarily intended to illustrate the general nature of the disclosure/described technology and, accordingly, the content of this Summary is not intended to be all-inclusive, and the scope of the disclosure is not limited to, or by, the exemplary aspects of the disclosure provided in this section. Any of the aspects described in this section can be combined with any other aspect described in this section or any other aspect of this disclosure.

Described here are new methods of performing geologic resource state determinations that can be performed by RVS methods or other rock volatile analytic methods that unexpectedly and surprisingly differ from or improve upon prior knowledge, including the methods of the '194 publication and '834 publication described in the Background.

Such differences can include, in aspects, expanding the scope of applicability of resource analysis, providing better matching of method(s) to type(s) of resource, providing methods of quantitative analysis of pressure, and providing methods that improve upon carbon dioxide-only related analyses. Such differences and benefits can be achieved through the quantification of pressure-related compound measurements based on the discovery that certain rock volatile compounds under certain conditions can act as reliable measures of geologic unit pressure. The methods of this technology are typically markedly more affordable, faster, and more widely applicable than traditional geologic pressure measurements, such as DST.

Thus, in exemplary aspects, this disclosure provides methods of determining geologic fluid pressure within at least a portion of a geologic unit (e.g., a play, a field, or a site/well) comprising determining the quantity of one or more pressure quantitative (which also may be presented as “pressure-quantitative”) rock volatiles (PQRVs) associated with one or more geologic materials (GMs) present in one or more specific locations (SLs) in the geologic unit (i.e., geologic unit specific location(s) (GUSLs)).

In further exemplary aspects, the disclosure provides methods of using predictive/quantitative analytical models of geologic unit pressure established by method(s) described herein to measure the pressure in at least a portion of a geologic unit comprising collecting at least one rock material sample (e.g., a drill cutting, core sample, etc.) from one or more specific locations of the geologic unit (sometimes just referred to as “locations”) where known pressure is desired; quantifying the amount of one or more pressure quantitative rock volatiles in each of the at least one rock material samples (e.g., easily extractable rock volatile carbon dioxide (EERVCD)); and comparing the quantified amount of the one or more pressure quantitative rock volatiles in each of the at least one rock material samples against the quantitative analytical model (QAM) of geologic unit pressure to obtain a pressure measurement within the geologic unit at each of the one or more specific locations of the geologic unit from which the rock material samples were collected. In aspects, such methods comprise analyzing the strength of the applicable input data set (comprising pressure quantitative rock volatile quantitative data (PQRVQD) to the model (i.e., the reliability or fit to the model). In aspects, data is used to determine pressure only where the fit is considered reliable/strong (e.g., is associated with an R-squared of at least 0.7). In aspects, when a fit is not sufficiently reliable/strong, the method further comprises modifying the data set (e.g., factoring in some/more pressure measurement adjusting (or “measurement-adjusting”) factor data (PMAFD), e.g., pressure measurement adjusting factor quantitative data (PMAFQD), e.g., by providing such PMAFQD as inputs to a/the QAM), making a new model, modifying the model, or some combination thereof.

In aspects, the disclosure provides methods of developing a quantitative analytical model of geologic unit pressure comprising obtaining at least two direct pressure measurements (DPMs) from at least two specific locations of the geologic unit (e.g., DST measurements of pressure) (as discussed in the CONSTRUCTION AND TERMS section of this disclosure the term “direct” here is not intended to limit the form of the other pressure measurement, but, rather to distinguish it from PQRVQD or PQRVQD-derived pressure measurements); obtaining the quantity of one or more pressure quantitative rock volatiles (PQRVs) from the specific locations or from about the same specific locations and evaluating the putative model by evaluating the fit/reliability of the PQRVQD to the direct pressure measurements and generating a quantitative analytical model (QAM) that at least reliably relates the quantity of the one or more pressure quantitative rock volatiles to the direct pressure measurements (provides a strong/good fit between the PQRVQD/PQRVQD derived data (PQRVQDDD) to the DPM. Standards for determining the reliability of a model/measure are discussed elsewhere. Such methods can comprise performing one or more iterations of modifying input to a putative model to arrive at a model that provides a reliable fit (e.g., removal of some PQRVQD from input).

In aspects, methods of this disclosure (aka/ac “methods”) comprise (1) evaluating if a putative quantitative analytical model (a putative analytical model (PAM)) or existing QAM/model indicates that the pressure quantitative rock volatile quantity data provides a reliable/high reliability measure of pressure in the one or more specific locations and (2) (a) removing some of the pressure quantitative rock volatile quantity data from the pressure quantitative rock volatile quantity included in the model, (b) incorporating pressure measurement adjusting factor (PMAF) data (PMAFD), pressure measurement adjusting factor indicator (PMAFI) data (PMAFID), or both, into the model as input data, or (c) both (a) and (b), and (d) repeating (a), (b), or (c) until the enhanced/revised model (enhanced QAM (EQAM)) indicates that the pressure quantitative rock volatile quantity data provides a high reliability measure of pressure in the one or more locations/specific locations (SLs).

In other aspects, methods also or alternatively comprise evaluating the suitability of different PQRVs for providing a reliable or highly reliable measure of pressure (e.g., in the context the method is performed in (type of geologic material, nature/composition/structure of the GU, or both)). As exemplified in FIG. 11, such a method can comprise evaluating the suitability/accuracy of easily extracted rock volatile carbon dioxide (EERVCD) as a/the selected PQRVD (alone or in combination with one, two, or more PMAFD(s)/PMAFID(s) as input(s)), and, in the event such an evaluation is found to not provide reliability or high reliability assessing other compound(s) that have been identified herein as PQRVDs (such as release resistant rock volatile carbon dioxide (RRRVCD), or rock volatile (RV) hydrogen (H₂), or RV helium). In aspects, EERVCD is the primary/main, near exclusive (generally/substantially only), or only PQRVD used in methods (again, alone or in combination with one, two, or more PMAFD(s)/PMAFID(s) as input data (e.g., RV water data, sulfate (e.g., sulfate/SO) data, or both)). The discovery that particular types of rock volatile carbon dioxide (RVCD) should be used for pressure determination (over any or all forms of RVCD) is just one way among others that the technology of this disclosure reflects an unexpected/surprising breakaway from the prior art, including the disclosures of the '194 publication and '834 publication.

Another unexpected aspect of technology of this disclosure, e.g., with respect to the disclosure of the '194 and '834 publications, is that in aspects PQRVs used in methods do not comprise certain carbon dioxide related compounds (CDRCs) (as that term is used in the '194 publication and mentioned in the Background) (e.g., formic acid, acetic acid, or both).

These surprising/unexpected findings contribute directly to some of the aspects of the methods provided herein (e.g., the various methods of quantifying pressure by use of PQRVQD alone or in combination with PMAFD/PMAFID (e.g., PMAFQD), unmodified or modified by enhancements/modifications of PQRVD or the model, etc., to make EQAM(s), and optionally with consideration of associated condition data (ACD(s)).

In aspects, any one or more of such methods can be implemented by/used to provide novel computer systems/devices, e.g., a computer system comprising (1) a computer processor, (2) memory, (3) an input component, and (4) an output component, wherein the computer systems/devices comprise one or more engines that are programmed to apply one or more quantitative analytical models of this disclosure or to generate putative quantitative analytical models. In aspects, artificial intelligence models/systems trained on PQRVD (alone or in association with PMAFD/PMAFID), and QAMs/PAMs can also be employed to provide or predict PQRVD pressure measurements, to generate additional QAMs/PAMs, or to generate proposed enhanced QAMs (EQAMs). Such computer systems represent yet another aspect of the technology.

These and additional new, surprising, unexpected, or advantageous embodiments of the technology and features or benefits thereof are further described, exemplified, or provided in the Detailed Description or elsewhere.

LISTED EXEMPLARY ASPECTS

A non-limiting list of exemplary aspects of the technology follows to illustrate certain embodiments in a concise manner and to aid readers in understanding ways such aspects may be combined with other aspects.

It is intended that these listed exemplary aspects begin with the first listed aspect (ASPECT 1) and thereafter be numbered sequentially and incrementally by the inclusion of a reference placed near or at the end of the listed aspect (ASPECT 2, ASPECT 3, etc.).

Similar to patent claims, these aspects listed in the paragraphs of this section may refer to (depend on/from) one or more other aspects referenced in other paragraphs. Readers will understand that such references mean that the features/characteristics or steps of such referenced aspects are incorporated into/combined with the referring aspect. For example, if an aspect in a paragraph (e.g., a paragraph indicated by text at the end of the paragraph as aspect 2) refers to another aspect by one or more aspect numbers (e.g., aspect 1 or “any one of aspects 1-3”), it will be understood to include the elements, steps, or characteristics of such referenced aspects (e.g., aspect 1) in addition to those of the aspect in which the reference is made (e.g., if aspect 2 refers to aspect 1, it provides a description of an object or method including the features of both aspect 1 and aspect 2).

Reference to ranges of aspects should be interpreted as referencing all such aspects individually, each as a unique embodiment, and in combination with one another as unique embodiment(s), according to the presentation provided of such aspects, unless such an aspect within such a referenced range is either contradictory or nonsensical. If contradicted, reference to the contradictory aspect should be considered to be excluded from the scope despite the inadvertent reference.

In any lengthy list of complex concepts, inadvertent reference errors can arise. In case of a missing aspect reference or repeated aspect reference, the order of placement of the actual recited aspect in the list that is associated with the repeated aspect reference or missing aspect reference will control (e.g., if there is an unlabeled aspect located between a first aspect labeled ASPECT 1 and a third aspect labeled aspect “ASPECT 2,” the unlabeled aspect should be treated as ASPECT 2, and the aspect labeled as ASPECT 2 treated as ASPECT 3, etc.), and all numbering in the list (including all references to aspects in the list) be interpreted as accordingly modified (e.g., if the fourth aspect in such list was labeled as ASPECT 3, it should be interpreted as being labeled as ASPECT 4, and if such aspect referred to “any one or both of aspect 1 or aspect 2,” it should be read as referring to “any one or more of aspects 1-3”). Similarly, if an aspect is misnumbered (e.g., by a number in the sequence being skipped or otherwise missing), readers will similarly construe this list of aspects according to the order of placement of the recited aspects, over the numerical references. Further, if one or more of the listed exemplary aspects listed in this section fails to reference any other aspects, such aspect, uncontradicted, should be interpreted as applying to or as capable of being incorporated into any other exemplary aspect(s) of a similar nature provided in this section or at least all preceding aspects of a similar nature.

In a first aspect, this disclosure provides methods of determining the pressure (e.g., geologic unit liquid pressure) within at least a portion of a geologic unit comprising determining the quantity of one or more pressure quantitative rock volatiles associated with one or more geologic materials of, or from, one or more specific locations within the geologic unit, based on a quantitative analytical model that relates (e.g., reliably relates) the quantity of the pressure quantitative rock volatiles to direct pressure measurement(s) in the GU. ASPECT 1

Readers should note that the term “specific locations” as described elsewhere may also be referred to herein by acronyms (SLs, GULs, or GUSLs) or simply called “locations.” Readers should be mindful of the possible meaning of the term “location” when interpreting it in this disclosure and consult the CONSTRUCTION AND TERMS section for additional helpful guidance concerning the interpretation of terms used in this and other sections.

Another aspect is a method of aspect 1, wherein the method comprises determining the pressure in one or more zones (e.g., formations), geologic units, or both, which are associated with geologic fluid resources. ASPECT 2

The disclosure also provides a method according to one or both of aspect 1 or aspect 2, wherein the method comprises obtaining geologic material samples from the one or more specific locations of the geologic unit and determining the quantity of the one or more pressure quantitative rock volatiles associated with each of the geologic material samples. ASPECT 3

A further aspect is a method of aspect 3, wherein the method comprises extracting the one or more pressure quantitative rock volatiles from each of the geologic material samples and subjecting the extracted pressure quantitative rock volatiles to one or more quantitative analysis techniques. ASPECT 4

The disclosure also provides a method according to the method of aspect 4, wherein the step of extracting the one or more pressure quantitative rock volatiles comprises, mostly comprises, generally consists of, or consists of applying a gentle vacuum pressure (e.g., a low gentle vacuum pressure) to the geologic material samples. ASPECT 5

A further aspect is a method of one or both of aspect 4 or aspect 5, wherein the one or more quantitative analysis techniques comprise cryogenic trap and release mass spectrometry analysis of the extracted pressure quantitative rock volatiles. ASPECT 6

Another aspect is a method of any one or more of aspects 3-6, wherein the samples of the one or more geologic materials mostly comprise, generally only comprise, are substantially composed of, or are entirely composed of rock material samples (e.g., are mostly, generally, substantially, or only made of drill cuttings). ASPECT 7

The disclosure also provides a method of any one or more of aspects 1-7, wherein the one or more pressure quantitative rock volatiles is/are mostly, generally, or only composed of rock volatile carbon dioxide (e.g., RRRVCD or total RVCD). ASPECT 8

A further aspect is a method of aspect 8, wherein the one or more pressure quantitative rock volatiles is/are mostly, generally, or only composed of easily extractable rock volatile carbon dioxide (EERVCD). ASPECT 9

Still, another aspect is a method according to any one or more of aspects 1-7, wherein the method comprises developing a quantitative analytical model that is designed to accurately calibrate the quantity of at least one of the one or more pressure quantitative rock volatile quantities quantified from the one or more geologic materials to direct pressure measurements from a number of specific locations of the geologic unit comprising comparing pressure quantitative rock volatile quantitative data (e.g., as raw data, as a refine data set, or as data derived therefrom/comprising such data) with a suitable amount of direct pressure measurements taken from or about the specific locations (“about” a location meaning an area that is about the same specific location, meaning a location which provides a sizably similar or significantly similar result(s) as the specific location, is within a zone that is less than 1.5× the size of the space/zone (or maximum or minimum end point) that defines the specific location (e.g., ˜0.1×-1.33×, 0.2-1.4×, 0.25-1.45×, or ˜0.3-1.5×) (e.g., within about 50 or about 60 ft of a 20-40 ft zone), or that varies from the zone/area of the specific location by less than 45%, 40%, or 33% of the distance between the specific location and the nearest specific location, e.g., the nearest specific location otherwise identified (e.g., from which a measurement is taken or a sample is collected) or less than 33% of the average distance between specific locations). ASPECT 10

The disclosure also provides a method of aspect 8, wherein the method comprises developing a quantitative analytical model that is demonstrated to at least reliably correlate the input data comprising the relationship between the quantity of rock volatile carbon dioxide (directly or in combination/relationship with other data such as the quantity of rock volatile water) in rock material samples from specific locations in the geologic unit to direct pressure measurements from or about the specific locations in the geologic unit. ASPECT 11

A further aspect is a method of aspect 11, wherein the method comprises (1) evaluating if the model indicates that the pressure quantitative rock volatile quantity data provides a reliable or high reliability measure of pressure in the one or more locations and (2) (a) removing some of the pressure quantitative rock volatile quantity data points from the pressure quantitative rock volatile quantity data set to be included in the model, (b) incorporating quantitative pressure measurement adjusting factor data, quantitative pressure measurement adjusting factor indicator data, or both into the model (e.g., as PMAFQD), or (c) both, and (d) repeating step(s) (a), (b), or (c) until the enhanced/revised model indicates that the pressure quantitative rock volatile quantity data provides a reliable or high reliability measure of pressure measurement from the input data in the one or more locations (SLs/GUSLs). ASPECT 12

The disclosure also provides a method of aspect 8, wherein the method comprises determining if an amount of easily extractable rock volatile carbon dioxide in the samples is able to provide a reliable quantitation of geologic fluid resource pressure and, if the amount of easily extractable rock volatile carbon dioxide in the samples cannot provide a reliable quantitation of geologic fluid resource pressure determining if release resistant rock volatile carbon dioxide can alternatively provide a reliable quantitation of geologic fluid resource pressure. ASPECT 13

The disclosure also provides a method of aspect 9, wherein the method comprises developing a quantitative analytical model that is designed to accurately calibrate the quantity of the rock volatile easily extractable carbon dioxide measured in the geologic unit to direct geologic fluid resource pressure measurements from the geologic unit. ASPECT 14

Another aspect is a method of any one or more of aspects 1-14, wherein at least one of the samples comprises a mixture of two or more rock types. ASPECT 15

A further aspect is a method of aspect 15, wherein the two or more rock types comprise two or three of the rock types comprising sandstones, limestones, and dolomites. ASPECT 16

The disclosure also provides a method of any one or more of aspects 10-16, wherein the method comprises (1) evaluating whether the inclusion of data associated with at least one quantitative pressure measurement adjusting factor or at least one pressure measurement adjusting factor indicator in the quantitative analytical model would detectably, sizably, majorly, or significantly improve the accuracy of the quantitative analytical model and, (2) if inclusion of data associated with the at least one pressure measurement adjusting factor or at least one pressure measurement adjusting factor indicator would detectably, sizably, majorly, or significantly improve the accuracy of the quantitative analytical model, incorporating quantitative data associated with the at least one pressure measurement adjusting factor, the at least one pressure measurement adjusting factor indicator, or both, in the quantitative analytical model. ASPECT 17

Another aspect is a method of aspect 17, wherein the at least one pressure measurement adjusting factor (PMAF) or at least one pressure measurement adjusting factor indicator (PMAFI) comprises a quantified rock volatile water (e.g., a capacitance manometry quantified rock volatile water), a rock volatile sulfate quantity (or, e.g., a quantity representative of a sulfate quantity, e.g., a mass spectrometry rock volatile sulfate quantity or mass spectrometry quantified sulfate proxy (sulfate/SO), or both. ASPECT 18

A further aspect is a method of aspect 18, wherein the method comprises (1) evaluating if incorporating data reflecting the concentration of water in the one or more specific locations may sizably or significantly positively impact the reliability of the rock volatile carbon dioxide quantity in determining pressure in the geologic unit and, (2) if so, incorporating rock volatile water quantity data into the model (e.g., by generating a PQRVQD derivative comprising a ratio of the PQRVQD to the quantity of rock volatile water for some, most, generally all, or all of the specific locations). ASPECT 19

The disclosure also provides a method of one or both of aspect 18 and aspect 19, wherein the method comprises using a ratio of rock volatile carbon dioxide quantity to rock volatile water quantity to evaluate geologic unit pressure. ASPECT 20

Yet additionally provided is a method of any one or more of aspects 17-20, wherein the at least one pressure measurement adjusting factor or at least one pressure measurement adjusting factor indicator comprises geologic unit water (e.g., rock volatile water), geologic unit/geologic material sulfate (directly or by proxy, e.g., measure of RV sulfate or proxy (sulfate/SO) thereof quantitative data), or both. ASPECT 21

The disclosure also provides a method of aspect 21, wherein the method comprises using a ratio of rock volatile water quantity to rock volatile carbon dioxide quantity to analyze pressure in the geologic unit. ASPECT 22

A further aspect is a method of any one or more of aspects 18-21, wherein the method comprises using a ratio comprising rock volatile carbon dioxide quantity, rock volatile water quantity, and rock volatile sulfate quantity or sulfate/SO quantity to evaluate geologic unit pressure. ASPECT 23

An additional aspect is a method of any one or more of aspects 17-21, wherein the at least one pressure measurement adjusting factor comprises total dissolved solids, ionic strength, pH, biogenic activity, salinity, temperature, or a combination of any or all thereof. ASPECT 24

The disclosure also provides a method of any one or more of aspects 17-24, wherein the at least one pressure measurement adjusting factor indicator comprises at least one organic acid, e.g., an organic acid that is an indicator of biogenic activity. ASPECT 25

Another aspect is a method of aspect 25, wherein the at least one organic acid comprises formic acid. ASPECT 26

Another aspect is a method of any one or more of aspects 8-26, wherein the one or more pressure quantitative rock volatiles are free of formic acid and acetic acid. ASPECT 27

A further aspect is a method of aspect 27, wherein the one or more pressure quantitative rock volatiles are free of bicarbonate, carbonic acid, or both. ASPECT 28

The disclosure also provides a method of aspect 18, wherein the amount of sulfate in the one or more locations is quantified by measuring the amount of rock volatile sulfate or sulfate proxy (sulfate/SO) via cryogenic trap and release mass spectrometry (“CTRMS”). ASPECT 29

Still another aspect is a method of any one or more of aspects 1-29, wherein the geologic unit comprises a prospective petroleum production site. ASPECT 30

A further aspect is a method of any one or more of aspects 1-29, wherein the geologic unit comprises a petroleum production site. ASPECT 31

The disclosure also provides a method of any one or more of aspects 1-29, wherein the geologic unit comprises a carbon sequestration site. ASPECT 32

An additional aspect is a method of any one or more of aspects 1-29, wherein the geologic unit comprises a region comprising two or more fields, each field comprising a plurality of prospective sites, production sites, or both. ASPECT 33

A further aspect is a method of any one or more of aspects 1-34, wherein the geologic unit comprises a tight rock area or tight formation. ASPECT 34

Still another aspect is a method of aspect 34, wherein the geologic unit comprises one or more horizontal wells associated with the tight rock area or tight formation. ASPECT 35

The disclosure also provides a method of any one or more of aspects 1-35, wherein the method comprises determining rock volatile carbon dioxide quantities in rock material samples from different specific locations within the geologic unit and evaluating if any difference in carbon dioxide quantity is indicative of a significant change in pressure in the geologic unit between the two specific locations. ASPECT 36

Another aspect is a method of any one or more of aspects 10-36, wherein the method comprises evaluating whether removing one or more data points of pressure quantifying rock volatile data that are associated with one or more geologic material (e.g., rock material) samples or specific locations from the geologic unit would increase the accuracy of the model and, if so, re-establishing the quantitative analytical model after excluding the data associated with the removed pressure quantifying rock volatile data from the model. ASPECT 37

A further aspect is a method of aspect 37, wherein the method comprises removing one or more data points associated with one or more specific locations or one or more geologic material (e.g., rock material) samples on a basis comprising a difference in salinity in the different rock volatile measurements obtained from measures taken in or from the different specific locations or rock material samples. ASPECT 38

Still another aspect is a method of any one or more of aspects 1-38, wherein the method comprises analyzing geologic material, e.g., rock material, samples collected from the geologic unit at different times (e.g., differing by >1 year, 5 years, 10 year, 20 years, or more (e.g., 5-50, 10-100, or 15-75 yrs.)) to evaluate pressure changes in the geologic unit over time. ASPECT 39

The disclosure also provides a method of aspect 39, wherein the method comprises predicting future pressure change in the geologic unit based on a model derived from the pressure changes in the geologic unit over time. ASPECT 40

An additional aspect is a method of any one or more of aspects 1-40, wherein the method comprises using the pressure determination(s) obtained by the method to direct resource extraction or utilization activities. ASPECT 41

A further aspect is a method of developing a quantitative analytical model of geologic unit pressure comprising obtaining at least two direct pressure measurements from at least two specific locations of the geologic unit, obtaining the quantity of one or more pressure quantitative rock volatiles, and generating a quantitative analytical model that reliably relates the quantity of the one or more pressure quantitative rock volatiles to the direct pressure measurements. ASPECT 42

Another aspect is a method of aspect 42, wherein the method further comprises evaluating if the inclusion of data associated with at least one pressure measurement adjusting factor or at least one pressure measurement adjusting factor indicator in the quantitative analytical model would detectably or significantly improve the accuracy of the quantitative analytical model and if inclusion of data associated with the at least one pressure measurement adjusting factor or at least one pressure measurement adjusting factor indicator would detectably or significantly improve the accuracy of the quantitative analytical model, including the data associated with the at least one pressure measurement adjusting factor or the at least one pressure measurement adjusting factor indicator in the model. ASPECT 43

An additional aspect is a method of one or both of aspects 42 and 43, wherein the one or more pressure quantitative rock volatiles comprises, mostly comprises, generally comprises, substantially consists of, or consists of carbon dioxide. ASPECT 44

One more aspect is a method of one or more of aspects 42-44, wherein the at least one pressure measurement adjusting factor, at least one pressure measurement adjusting factor indicator, or both, comprises, generally comprises, substantially consists of, or consists of water, sulfur monoxide, or both. ASPECT 45

Another facet is a method of any one or more of aspects 42-45, wherein the model is at least reliable, or highly reliable, when applied to geologic materials taken from different formations within the geologic unit, separate sites within the geologic unit, or both. ASPECT 46

The technology also provides a method of aspect 46, wherein the different sites are different wells, and the geologic unit is a field, a play, or a system (a basin or province). ASPECT 47

Further provided is a method according to aspect 47, wherein the different sites comprise one or more sites located in different fields in a play or system. ASPECT 48

The invention also provides a method of any one or more of aspects 42-48, wherein a step in developing the quantitative model comprises determining if there is a correlation between the quantity of one or more pressure quantitative rock volatiles (alone or in a relationship with one or more initial pressure measurement adjusting factor quantitative data measurements) and one or more putative items of potential pressure measurement adjusting factor quantitative data and if there is a correlation testing a putative analytical model that incorporates the one or more putative items of potential pressure measurement adjusting factor quantitative data, to determine if the putative analytical model is a quantitative analytical model that provides reliable geologic unit specific location pressure measurements (GUSLPMs). ASPECT 49

Still another aspect is a method of using the quantitative analytical model of geologic unit pressure established by one or more of aspects 42-49 to measure the pressure in at least a portion of a geologic unit comprising collecting at least one geologic material sample, e.g., rock material sample, from one or more specific locations of the geologic unit where known pressure is desired; quantifying the amount of one or more pressure quantitative rock volatiles in each of the at least one geologic material, e.g., rock material, samples; and comparing the quantified amount of the one or more pressure quantitative rock volatiles in each of the at least one geologic material, e.g., rock material, samples against the quantitative analytical model of geologic unit pressure to obtain a measured pressure within the geologic unit at each of the one or more specific locations of the geologic unit from which the geologic material, e.g., rock material samples were collected. ASPECT 50

The disclosure also provides a method according to any one or more of aspects 1-50, wherein the geologic material sample or samples, e.g., rock material sample or samples, comprise, mostly comprise, generally only comprise, substantially consist of, or consist of well/borehole drill cuttings. ASPECT 51

Further provided is a method of any one or more of aspects 1-51, wherein the method comprises providing at least 10, 20, 30, 40, 50, 100, or more, e.g., 5-500, 5-250, 10-200, or 15-300 pressure measurements for a corresponding number or approximately corresponding number of specific locations within the at least portion of the geologic unit based, in at least part, on the quantity of the one or more pressure quantitative rock volatiles measured in the method. ASPECT 52

Also provided is a method according to aspect 52, wherein the pressure measurements are made within a period of less than 1.5 hours, e.g., less than 1.25, less than 1, or less than 0.75 hrs. (e.g., 0.1-2 hrs., 0.2-3 hours, 0.2-2 hrs., 0.25-2.5 hrs., 0.3-3 hrs., 0.4-2 hrs., 0.5-1.5 hrs., etc.). ASPECT 53

Further provided is a method of any one or more of aspects 1-53, wherein the method comprises forming a relationship, such as a ratio, between the at least one pressure quantitative rock volatile quantitative data and at least one pressure measurement adjusting factor quantitative data and using the relationship in accordance with an at least reliable quantitative analytical model to determine, at least in part, the fluid pressure in the one or more specific locations. ASPECT 54

The technology also provides a method of aspect 54, wherein the at least one pressure measurement adjusting factor quantitative data is associated with a pressure measurement adjusting factor or a pressure measurement adjusting factor indicator that is associated with the concentration of the pressure quantitative rock volatile measured in the pressure quantitative rock volatile quantitative data. ASPECT 55

Additionally provided is a method of one or both of aspect 54 or aspect 55, wherein the method comprises using quantitative data associated with an at least second pressure measurement adjusting factor in the model, wherein the inclusion of the at least second pressure measurement adjusting factor sizably or significantly enhances the reliability of the model. ASPECT 56

The technology also includes new computer systems comprising (1) a computer processor, (2) memory, (3) an input component, and (4) an output component), wherein the computer system comprises one or more program(s)/engine(s) that are programmed to apply any one or more of quantitative analytical models described or referenced in any one of aspects 1-56 to pressure quantitative rock volatile quantity data, data derived therefrom (e.g., a ratio of PQRVQD(s) and PMAFQD(s)), or both. ASPECT 57

An additional aspect is a computer system of aspect 57, wherein the computer system is programmed to selectively, automatically, selectively automatically, or a combination thereof, factor one or more quantitative analytical models that factor in pressure measurement adjusting factor data, pressure measurement adjusting factor indicator data, or both, into the quantitative analytical model. ASPECT 58

Another aspect is a computer system of any one or both of aspect 57 or aspect 58, wherein the computer system comprises an artificial intelligence system/model (“system”) that is trained on data sets comprising direct pressure measurement data associated with a type of geologic unit and associated pressure quantitative rock volatile quantity data and permitting the artificial intelligence system to analyze pressure quantitative rock volatile quantity data to automatically or semi-automatically provide pressure quantitation data based on the analysis performed by the artificial intelligence system. ASPECT 59

As discussed herein, aspects of the technology also can be described as “means for” or “steps for” providing features or functions, respectively (the construction of such terms is known and further described in the CONSTRUCTION AND TERMS section of this document) (these terms, “step” and “means” may be used interchangeably herein). Examples of such aspects include a method of determining the pressure within at least a portion of a geologic unit comprising a step for determining the quantity of one or more pressure quantitative rock volatiles associated with one or more geologic materials of or from one or more specific locations within the geologic unit based on a quantitative analytical model that relates the quantity of the pressure quantitative rock volatiles to pressure in the geologic unit (e.g., performing in situ RV measurements or performing RV extraction and extracted RV quantification). ASPECT 60

A further example is a method of aspect 60, wherein the method specifically comprises (1) a step for extracting the one or more pressure quantitative rock volatiles from one or more geologic materials, e.g., rock materials, that the one or more geologic materials comprise (e.g., by gentle vacuum extraction or by application of a gentle vacuum equivalent force to similarly extract the RVs in a suitable manner) and (2) a step for quantifying the quantity of the one or more pressure quantitative rock volatiles in the one or more geologic materials, e.g., rock materials. ASPECT 61

Another facet is a method of aspect 61, wherein the method comprises (1) a step for trapping at least some of the one or more rock volatiles (e.g., in a cryotrap or other trapping system), (2) a step for releasing at least some of the at least some trapped rock volatiles as a plurality of released materials in a manner that separates the content or quantity of the rock volatiles in the different portions (e.g., chromatographic release, temperature release from a cryotrap, etc.), (3) a step for analyzing the content and quantity of the rock volatiles in the different portions, (4) a step for relaying data reflecting the analyzed content of the rock volatiles in the different portions to one or more systems or persons involved in geologic material operations (e.g., by automatic or semi-automatic email, text message, or similar data relay, etc.), and (5) a step for performing geologic material operations that are guided at least in part by the relayed data (e.g., guiding exploration, guiding production, or guiding sequestration activities, etc.). ASPECT 62

The technology also provides a method according to any one or more of aspects 60-62, wherein the method further comprises (1) a step for determining the reliability of the quantity of the pressure quantitative rock volatiles to act as an independent predictor of geologic unit specific location pressure, (2) a step for obtaining pressure measurement adjusting factor quantitative data from the one or more geologic materials, (3) a step for evaluating if incorporating the pressure measurement adjusting factor quantitative data in a model with the quantity of the one or more pressure quantitative rock volatiles results in a reliable model or a more reliable model (e.g., inputting the PMAFQD into a regression model, such as a least squares linear regression model along with the PQRVD or PQRVD-related data (e.g., a PQRVQD/PMAFQD ratio) and, if so, (4) incorporating the pressure measurement adjusting factor quantitative data into the model to generate an enhanced quantitative analytical model and using the enhanced quantitative analytical model to measure the fluid pressure of the one or more geologic unit specific locations. ASPECT 63

In aspects, the disclosure provides a method of determining fluid pressure in a geologic unit comprising determining the quantity of one or more pressure quantitative rock volatiles in rock materials in or from one or more specific locations of the geologic unit and using the quantity of the one or more pressure quantitative rock values to determine the quantity of geologic unit fluid pressure based on a quantitative analytical model that relates the quantity of the one or more pressure quantitative rock volatiles to the quantity of the geologic unit fluid pressure. ASPECT 64

In aspects, the disclosure provides the method of aspect 64, wherein the method comprises using a ratio of the quantity of the pressure quantitative rock volatile quantity to the quantity of rock volatile water measured in the one or more specific locations to determine the geologic unit fluid pressure. ASPECT 65

In aspects, the disclosure provides the method of one or both of aspect 64 and aspect 65, wherein the method comprises collecting pressure quantitative rock volatile quantity data from two or more specific locations in the geologic unit and determining the fluid pressure at the two or more specific locations. ASPECT 66

In aspects, the disclosure provides the method of one or more of aspects 64-66, wherein the method comprises improving the fit of the data set for the quantity of the pressure quantitative rock volatiles to the model by selectively removing pressure quantitative rock volatile data from the data set input to the model based on one or more factors that indicate that such pressure quantitative rock volatile data originates from rock material that is not as reliably correlated to geologic unit fluid pressure by the model. ASPECT 67

In aspects, the disclosure provides the method of one or more of aspects 64-67, wherein the method comprises improving the measurement of geologic unit fluid pressure by introducing quantitative data for one or more secondary pressure measurement adjusting factors into the model. ASPECT 68

In aspects, the disclosure provides the method of one or more of aspects 64-68, wherein the model is a linear regression model. ASPECT 69

In aspects, the disclosure provides the method of one or more of aspects 64-69, wherein the method comprises collecting rock material samples from the two or more specific locations, extracting the pressure quantitative rock volatiles from the samples, measuring the quantity of one or more extracted pressure quantitative rock volatiles, and quantifying the amount of the one or more pressure quantitative rock volatiles associated with each specific location based on the quantity of the extracted pressure quantitative rock volatiles. ASPECT 70

In aspects, the disclosure provides the method of one or more of aspects 64-70, wherein the method comprises collecting rock material samples from 20 or more specific locations, extracting the pressure quantitative rock volatiles from the 20 or more samples, measuring the quantity of one or more extracted pressure quantitative rock volatiles for each of the 20 or more samples, and quantifying the amount of the one or more pressure quantitative rock volatiles associated with each specific location based on the quantity of the extracted pressure quantitative rock volatiles from each of the 20 or more samples. ASPECT 71

In aspects, the disclosure provides the method of one or more of aspects 64-71, wherein the method comprises applying a gentle vacuum to the samples to extract the pressure quantitative rock volatiles and performing analysis comprising cryogenic trap-and-release mass spectrometry to quantify the extracted pressure quantitative rock volatiles. ASPECT 72

In aspects, the disclosure provides the method of one or both of aspect 71 and aspect 72, wherein the 20 or more samples comprise samples taken from a tight formation, samples taken from a horizontal borehole, or both. ASPECT 73

In aspects, the disclosure provides the method of one or more of aspects 71-73, wherein the 20 or more samples are collected from two or more separated zones or geologic subunits within the geologic unit, and the method comprises evaluating the pressure of the two or more separated zones or geologic subunits. ASPECT 74

In aspects, the disclosure provides the method of one or more of aspects 71-74, wherein the 20 or more samples are collected at different times, wherein the different times vary by a period of one or more years, and wherein the method comprises evaluating changes in fluid pressure in the geologic unit over the time period. ASPECT 75

In aspects, the disclosure provides the method of aspect 75, wherein the method comprises using the changes in fluid pressure in the geologic unit over the time period to predict what the fluid pressure in the geologic unit will be at a future time point. ASPECT 76

In aspects, the disclosure provides the method of one or more of aspects 71-76, wherein the geologic unit fluid pressures determined by the quantity of the extracted pressure quantitative rock volatiles are provided as data used to guide one or more decisions regarding geologic resource utilization in the geologic unit. ASPECT 77

In aspects, the disclosure provides the method of aspect 77, wherein the one or more decisions regarding geologic resource utilization comprise one or more decisions relating to high-energy hydrocarbon exploration, production, or both. ASPECT 78

In aspects, the disclosure provides the method of aspect 78, wherein the one or more decisions relating to high-energy hydrocarbon exploration, production, or both comprise one or more decisions regarding the application of enhanced oil recovery methods to a portion of the geologic unit. ASPECT 79

In aspects, the disclosure provides the method of one or more of aspects 77-79, wherein the one or more decisions regarding geologic resource utilization comprise one or more decisions concerning the use of at least a portion of the geologic unit as a carbon sequestration site. ASPECT 80

In certain respects, the disclosure provides a computer system comprising a computer processor that is programmed to receive and recognize pressure quantitative rock volatile quantitative data and pressure measurement adjusting quantitative data and that is further programmed to perform the methods of one or more method aspects disclosed in this section. ASPECT 81

In certain respects, the disclosure provides the computer system of aspect 81, wherein the computer system comprises an artificial intelligence component that aids in the performance of one or more activity(ies) disclosed here. ASPECT 82

According to certain facets, the disclosure provides a method of generating and utilizing a model for correlating pressure quantitative rock volatile quantity to geologic liquid pressure comprising (1) obtaining two or more direct pressure measurements in a geologic unit from two or more specific locations of the geologic unit, (2) obtaining two or more pressure quantifying rock volatile quantity measurements from at or near the two or more specific locations, (3) inputting the two or more direct pressure measurements of the geologic unit and the two or more pressure quantifying rock volatile quantity measurements into a mathematical model, (4) determining the fit of the correlation of the two or more direct pressure measurements of the geologic unit and the two or more pressure quantifying rock volatile quantity measurements, and (5) if the correlation of the two or more direct pressure measurements from the geologic unit and the two or more pressure quantifying rock volatile quantity measurements indicates the model is a reliable model using the model to quantify geologic fluid pressure measurements from one or more other specific locations in the geologic unit based on one or more other pressure quantifying rock volatile quantities measured in or from the one or more other specific locations. ASPECT 83

In certain aspects, the invention provides a method of determining geologic fluid pressure in a geologic unit comprising (1) a step for collecting one or more pressure quantitative rock volatile quantities from one or more specific locations of a geologic unit, (2) a step for correlating the one or more pressure quantitative rock volatile quantities with a model that is capable of quantifying the liquid pressure at a geologic unit location based on at least some pressure quantitative rock volatile measurements to determine if the one or more pressure quantitative rock volatile measurements are a good fit for the model, and (3) if there is a reliable correlation with the one or more pressure quantitative rock volatile measurements and model, quantifying the fluid pressure at the one or more specific locations based on the one or more pressure quantitative rock volatile quantities. ASPECT 84

In certain aspects, the invention provides the method of one or more of the method aspects disclosed herein, wherein the method comprises applying gentle vacuum to the samples to extract one or more, e.g., a plurality of rock volatiles and performing analysis comprising (a) mass-spectrophotometric quantification of one or more rock volatiles to quantify one or more rock volatiles; (b) capacitance manometry quantification of one or more rock volatiles to quantify one or more rock volatiles; or (c) performing each of mass-spectrophotometric quantification and capacitance manometry quantification to quantify different extracted rock volatiles of a plurality of extracted rock volatiles. ASPECT 85

Further exemplary aspects can include:

A method of determining fluid pressure in a geologic unit comprising determining the quantity of one or more pressure quantitative rock volatiles in rock materials in or from one or more locations of the geologic unit and using the quantity of the one or more pressure quantitative rock values to determine the quantity of geologic unit fluid pressure based on a quantitative analytical model that relates the quantity of the one or more pressure quantitative rock volatiles to the quantity of the geologic unit fluid pressure. ASPECT A

The method of ASPECT A, wherein the method comprises using a ratio of the quantity of the pressure quantitative rock volatile quantity to the quantity of rock volatile water measured in the one or more specific locations to determine the geologic unit fluid pressure. ASPECT B

The method of ASPECT B, wherein the method comprises collecting pressure quantitative rock volatile quantity data from two or more specific locations in the geologic unit and determining the fluid pressure at the two or more specific locations. ASPECT C

The method of ASPECT C, wherein the method comprises improving the fit of the data set for the quantity of the pressure quantitative rock volatiles to the model by selectively removing pressure quantitative rock volatile data from the data set input to the model based on one or more factors that indicate that such pressure quantitative rock volatile data originates from rock material that is not as reliably correlated to geologic unit fluid pressure by the model. ASPECT D

The method of any suitable preceding ASPECT, wherein the method comprises improving the measurement of geologic unit fluid pressure by introducing quantitative data for one or more secondary pressure measurement adjusting factors into the model. ASPECT E

The method of any suitable preceding ASPECT, wherein the model is a linear regression model. ASPECT F

The method of any suitable preceding ASPECT, wherein the method comprises collecting rock material samples from the two or more specific locations, extracting the pressure quantitative rock volatiles from the samples, measuring the quantity of one or more extracted pressure quantitative rock volatiles, and quantifying the amount of the one or more pressure quantitative rock volatiles associated with each specific location based on the quantity of the extracted pressure quantitative rock volatiles. ASPECT G

The method of any suitable preceding ASPECT, wherein the method comprises collecting rock material samples from 20 or more specific locations, extracting the pressure quantitative rock volatiles from the 20 or more samples, measuring the quantity of one or more extracted pressure quantitative rock volatiles for each of the 20 or more samples, and quantifying the amount of the one or more pressure quantitative rock volatiles associated with each specific location based on the quantity of the extracted pressure quantitative rock volatiles from each of the 20 or more samples. ASPECT H

The method of any suitable preceding ASPECT, wherein the method comprises applying gentle vacuum to the samples to extract the pressure quantitative rock volatiles and performing cryogenic trap-and-release mass spectrometry to quantify the extracted pressure quantitative rock volatiles. ASPECT I

The method of any suitable preceding ASPECT, wherein the 20 or more samples comprise samples taken from a tight formation, samples taken from a horizontal borehole, or both. ASPECT J

The method of any suitable preceding ASPECT, wherein the 20 or more samples are collected from two or more separated zones or geologic subunits within the geologic unit, and the method comprises evaluating the pressure of the two or more separated zones or geologic subunits. ASPECT K

The method of any suitable preceding ASPECT, wherein the 20 or more samples are collected at different times, wherein the different times vary by a period of one or more years, and wherein the method comprises evaluating changes in fluid pressure in the geologic unit over the time period. ASPECT L

The method of any suitable preceding ASPECT, wherein the method comprises using the changes in fluid pressure in the geologic unit over the time period to predict what the fluid pressure in the geologic unit will be at a future time point. ASPECT M

The method of any suitable preceding ASPECT, wherein the geologic unit fluid pressures determined by the quantity of the extracted pressure quantitative rock volatiles are provided as data used to guide one or more decisions regarding geologic resource utilization in the geologic unit. ASPECT N

The method of any suitable preceding ASPECT, wherein the one or more decisions regarding geologic resource utilization comprise one or more decisions relating to high-energy hydrocarbon exploration, production, or both. ASPECT O

The method of any suitable preceding ASPECT, wherein the one or more decisions relating to high energy hydrocarbon exploration, production, or both comprise one or more decisions regarding the application of enhanced oil recovery methods to a portion of the geologic unit. ASPECT P

The method of any suitable preceding ASPECT, wherein the one or more decisions regarding geologic resource utilization comprise one or more decisions concerning the use of at least a portion of the geologic unit as a carbon sequestration site. ASPECT Q

A computer system comprising a computer processor that is programmed to receive and recognize pressure quantitative rock volatile quantitative data and pressure measurement adjusting quantitative data and that is further programmed to perform the method of any of the preceding ASPECTS. ASPECT R

The computer system of ASPECT R, wherein the computer system comprises an artificial intelligence component that aids in or carries out the performance of the method. ASPECT S

A method of generating and utilizing a model for correlating pressure quantitative rock volatile quantity to geologic liquid pressure comprising (1) obtaining two or more direct pressure measurements in a geologic unit from two or more specific locations of the geologic unit, (2) obtaining two or more pressure quantifying rock volatile quantity measurements from the two or more specific locations, (3) inputting the two or more direct pressure measurements in a geologic unit and the two or more pressure quantifying rock volatile quantity measurements into a mathematical model, (4) determining the fit of the correlation of the two or more direct pressure measurements in a geologic unit and the two or more pressure quantifying rock volatile quantity measurements, and (5) if the correlation of the two or more direct pressure measurements in a geologic unit and the two or more pressure quantifying rock volatile quantity measurements indicates the model is a reliable model using the model to quantify geologic fluid pressure measurements from one or more other specific locations in the geologic unit based on one or more other pressure quantifying rock volatile quantities measured in or from the one or more other specific locations. ASPECT T

A method of determining geologic fluid pressure in a geologic unit comprising (1) a step for collecting one or more pressure quantitative rock volatile quantities from one or more specific locations of a geologic unit, (2) a step for correlating the one or more pressure quantitative rock volatile quantities with a model that is capable of quantifying the liquid pressure at a geologic unit location based on at least some pressure quantitative rock volatile measurements to determine if the one or more pressure quantitative rock volatile measurements are a good fit for the model, and (3) if there is a reliable correlation with the one or more pressure quantitative rock volatile measurements and model, quantifying the fluid pressure at the one or more specific locations based on the one or more pressure quantitative rock volatile quantities. ASPECT U.

Further exemplary aspects can include and/or the above described aspects can be restated as—

A method of determining fluid pressure in a geologic unit comprising (1) determining the quantity of one or more pressure-quantitative rock volatiles in rock material located in or obtained from one or more specific locations of the geologic unit to form a data set of pressure-quantitative rock volatile measurements, (2) selecting or generating a quantitative analytical model that relates the quantity of the one or more pressure-quantitative rock volatiles in the data set to the quantity of the geologic unit fluid pressure, and (3) applying the quantity of the one or more pressure-quantitative rock values to the quantitative analytical model to generate an output from the model and (4) using the output to determine the quantity of geologic unit fluid pressure in the geologic unit. ASPECT #I

The method of ASPECT #I, wherein the method comprises generating a ratio of the geologic unit fluid pressure determined in step (4) to the quantity of rock volatile water measured in the one or more specific locations and using the ratio as an input to the model. ASPECT #II

The method of any preceding applicable ASPECT, wherein the method comprises collecting one or more pressure data associated with the one or more specific locations and comparing the output generated in step (3) of the method to the one or more pressure data. ASPECT #III

The method of any preceding applicable ASPECT, wherein the method comprises improving a fit of the pressure-quantitative rock volatiles to the model by selectively removing one or more pressure-quantitative rock volatile data from the data set based on factor(s) that indicate that such pressure-quantitative rock volatile data originates from rock material that is not as reliably correlated to geologic unit fluid pressure by the model. ASPECT #IV

The method of any preceding applicable ASPECT, wherein the method comprises improving the measurement of geologic unit fluid pressure by factoring quantitative data for one or more secondary pressure measurement-adjusting factors into the model. ASPECT #V.

Any of the above-describe aspects can be combined with one another in any suitable manner and may further also or alternatively be combined with any aspect of the technology described below or illustrated in the drawings/figures provided herewith.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The drawings/figures provided here, and the associated following brief description of figures, are intended to exemplify certain aspects and principles of this disclosure without limiting its scope.

FIG. 1 illustrates a comparison of the ease of water release from samples collected from 2 wells, the ease of water release being calculated from data obtained from the analysis of samples collected from the two wells.

FIG. 2A illustrates the comparison of CO₂ data obtained from the analysis of the samples collected from the two wells represented in FIG. 1 (which may be alternatively referred to as “FIG. 1”).

FIG. 2B provides an alternative view of the data presented in FIG. 2A.

FIG. 2C provides an alternative view of the data presented in FIGS. 2A and 2B.

FIG. 3A illustrates well pressure(s) in four different formations located at various depths of a single well obtained by Drill Stem Testing (DST), compared to the amount of CO₂ measured by Rock Volatile Stratigraphy (RVS) in drill cutting samples collected from the formations at such depths.

FIG. 3B provides an alternative view of the data presented in FIG. 3A, providing the mean CO₂ measured by the RVS method in samples surrounding the depths of the well at which the DST pressure measurements were taken.

FIG. 4 illustrates a calculated ratio of CO₂ to water plotted against the pressure measured by DST for samples collected from a single formation within four different wells that have experienced different amounts of production over time in the field.

FIG. 5 illustrates a calculated ratio of CO₂ to water plotted against the pressure measured by a technology similar to DST for samples collected from 4 different formations within one of the same wells presented in FIGS. 1 and 2.

FIG. 6 illustrates additional data from the samples of FIGS. 3A (and 3B), wherein a calculated ratio of CO₂ to water is plotted against the pressure measured by DST for samples collected from the four different formations within the well, and such data is combined with the data from FIG. 5, with a linear regression applied to the collective data.

FIG. 7 illustrates a plot of sulfate proxy data (where sulfate is, e.g., oppositionally correlated (or oppositely correlated or inversely correlated) to salinity and thus such sulfate proxy data is indirectly reflective of salinity) compared to the calculated ratio of CO₂ to water in samples from the same well, as represented in FIGS. 3A-3B.

FIG. 8 provides a plot of data presented in prior figures, with data from deep saline aquifers excluded.

FIG. 9A illustrates the measured sulfate proxy across various formations of two different fields described in the previously listed Figures.

FIG. 9B illustrates the incorporation of data from FIG. 9A into previously presented CO₂ to water ratio data (previously presented) and a plot of the same against pressure.

FIG. 10 is a flow chart of the steps of an exemplary procedure of an exemplary method of this disclosure, describing the evaluation of PQRVD in a model, possible enhancement of the model, and application thereof to determine pressure in at least a portion of a geologic unit (“GU”).

FIG. 11 is a flow chart of the steps of an exemplary procedure for the analysis of candidate PQRVs and associated PQRV data (“PQRVD”) to arrive at a highly reliable model for determining GU pressure (“GUP”) from PQRVD (alone or in combination with other data, such as PMAF/PMAFI data).

DETAILED DESCRIPTION

To illuminate the scope, nature, and operation of the technology (ies), descriptions of overall embodiments (complete methods, objects, etc.), combinations of elements/steps that make up such embodiments, and individual elements/steps of such embodiments may be provided in this Detailed Description. Readers will nonetheless understand that despite the inclusion of passages focused on specific elements/steps of embodiments, uncontradicted, any description of particular step(s) or element(s) can be applied to/combined with any description of a larger complete embodiment, combination of elements, or any other specifically recited element(s)/step(s) thereof, which are provided in any part of this disclosure.

A breakthrough that forms a basis for several aspects of this disclosure is the discovery that the quantity of certain rock volatile compounds (“volatiles”) can provide a quantitative measure of the pressure in at least a portion of a geologic unit, such as in a geologic fluid resource. That breakthrough has led to the development of new and unexpected methods of determining the pressure within at least a portion of a geologic unit, comprising determining the quantity of one or more of such pressure quantitative rock volatiles (PQRVs) associated with one or more geologic materials of or from one or more specific locations within the geologic unit(s) of interest. PQRV quantity (PQRV data quantity(ies) (DQ); PQRVQD) is, directly or through derivative data comprising PQRVQD, in contexts, correlated to GU liquid pressure, as can be demonstrated through a suitable quantitative analytical model (QAM) (sometimes aka/ac a “model”). A model/QAM can be, e.g., a regression model, such as a linear regression model, e.g., a least squares regression model, such as a model implemented in the LINEST function in Microsoft Excel, which demonstrates an at least reliable correlation between the quantity of one or more PQRV(s) and geologic unit pressure. Where a suitable QAM/model exists, the quantitative measurement of a PQRV can provide a basis for determining geologic unit pressure. The evaluation of the applicability of the model can be performed through any suitable means, such as by evaluation of the strength/fit of input data (including, e.g., PQRVQD, PQRVQDDD, PQRVQD, and PQRVQDDD, with or without, e.g., PMAFQD) to a model that relates PQRVQD/PQRVQDDD to direct pressure measurement(s) (DPM(s)). One surprising aspect of the technology is that PQRVQD/PQRVQDDD to DPM models “hold” (i.e., are applicable) across many contexts (e.g., across different fields, across different formations, or over markedly different time periods).

A more particular breakthrough in this respect is the discovery that the quantity of rock volatile carbon dioxide (CO₂ or CO₂) measured from geologic materials (GMs) from geologic unit-specific locations (GUSLs/GULs) can serve as a quantitative indicator of GUSL/GUSL-associated pressure (e.g., petroleum reservoir pressure). Accordingly, in aspects, most, generally all, or all of the PVQR data used in a method is rock volatile carbon dioxide quantitative data (RVCDQD). Still, an even further unexpected and more particular breakthrough is that in certain aspects, a model will only perform or will perform sizably, majorly, or significantly better if only certain carbon dioxide volatiles are included in the quantitative pressure analysis/determination. One finding is that in most aspects, easily extractable rock volatile carbon dioxide (EERVCD) provides a better quantification than release resistant rock volatile carbon dioxide (RRRVCD), but that, in certain and less common contexts where EERVCD does not provide a reliable or highly reliable pressure quantification measure (HRPM), RRRVCD data may provide a superior pressure quantification. Accordingly, methods can comprise the use of either type of carbon dioxide or the evaluation of the suitability of one or the other in providing a quantitative pressure measurement.

An additional breakthrough is the discovery that the ability of PQRV(s) to provide a reliable or highly reliable quantitative can be influenced by introducing data associated with one or more pressure measurement affecting factors (PMAF(s)) or PMAF indicator(s) (PMAFI(s) or “proxies”) into the model.

Pressure measurement adjusting factors (PMAFs) are factors that are not capable of acting as a quantifier of GU pressure alone, but can detectably, sizably, majorly, or significantly increase the reliability of PQRV data (PQRVD) when suitably factored into a model with such pressure quantitative rock volatile quantitative data. In one aspect, a PMAF (or an indicator of the existence/quantity of a PAMF—a PMAFI) used in a method is associated with quantitative data (PMAFQD), through incorporating PMAF data (PMAFD) or PMAFID into the model (PMAF or PMAFI data incorporated into a model is often also referred to herein as PMAFIDQ). In one aspect, the introduction of PMAF/PMAFI data (PMAFQD/PMAFIDQ) in the model is achieved by obtaining quantitative PMAF data (PMAFD)/PMAFID and generating a relationship, e.g., a ratio, comprising one, two, or more PMAFD/PMAFID values and PQRV data (PQRVD). For example, rock volatile water, rock volatile sulfate/SO (see description elsewhere herein), or both, can be used, e.g., alone (in certain aspects) or in a ratio (in certain aspects) with RVCDQD, e.g., EERVCDQD, to provide an enhanced model and sizably, majorly, or statistically superior reliability over relying solely on the applicable RVCDQD.

Aspects of such methods can also include evaluating if the removal of certain PQRVD from a PQRV data set (DS) can detectably, sizably, majorly, or significantly enhance the reliability of the PQRVD or PQRVD/PMAFD-PMAFID in quantifying pressure. E.g., methods can comprise removal/exclusion of outlier data or data from specific locations that have markedly different properties (or where the geologic material (GM) therein/thereof have markedly different properties—e.g., composition, structure, etc.) from some, a considerable amount, most, or generally all of the other PQRVD or other evaluated locations/materials. Differences in properties, e.g., data defining or establishing or associated with differences in properties, may be referred to herein as associated condition(s)/associated condition data (ACD).

Another facet of the technology is the provision of new methods of developing a quantitative analytical model (QAM) of geologic unit pressure based on PQRVD. Such methods comprise obtaining at least two independent/direct pressure measurements (i.e., pressure measurements that do not involve PQRVD) (e.g., “direct” fluid/GU pressure measurements—such as from DST data, data from a step rate breakdown test (including, e.g., non-traditional breakdown test data obtained before the application of pressure in the breakdown testing), RFT/MDT tests, reservoir bottom-hole pressure buildup tests, and other hydrostatic pressure measurement tools/methods, etc.) from at least two locations of the geologic unit, obtaining PQRVD, and generating a quantitative analytical model that reliably relates the quantity of the PQRVD to the pressure measurements to generate a putative model, which can be evaluated to determine if the model is a QAM. Such methods can further comprise evaluating if the model can be enhanced by factoring in PMAFD/PMAFID, excluding certain PQRVD, or both to establish an enhanced QAM (EQAM).

Yet another facet of the technology stemming from the above-described breakthroughs and other aspects described herein is the provision of new computer devices/systems (e.g., comprising (1) a computer processor, (2) memory, (3) an input component, and (4) an output component), wherein the computer processor comprises a computer program/engine that is programmed to apply one or more quantitative analytical models (QAM(s)) (or EQAM(s)) described above to PQRVD to provide a reliable/highly reliable model or to provide PQRVD-derived pressure measurements and to evaluate the same for reliability. In aspects, artificial intelligence models and systems (e.g., neural network models/systems) can be trained using PQRVD and various models, possibly in combination with other inputs such as PMAFID (PMAFIDQ) or associated condition data (ACD), to provide measures/predictions of pressure in the applicable GULs from measured PQRVs. For example, an AI model trained with prior analyzed/validated data and model(s) may predict pressure(s) based on new raw data presented to the AI model. Computer devices/systems and also or alternatively artificial intelligence models and systems of the technology may also be used to determine what geological material related data should and should not be used with particular QAMs to establish pressure, and both which QAM(s) and how many QAMs are appropriate for use given certain geological material related data in determining reliable pressure measurements for location(s) represented by geological material sample(s).

To better illustrate the features of these and other aspects, the following subsections of this Detailed Description will primarily focus specifically on such features/elements while also, at points, referring to other elements, or whole embodiments.

Rock Volatiles

A facet of many of the technologies described herein is the use of rock volatile compounds (aka, “rock volatiles” or simply “volatiles”). For example, in aspects, as discussed further below, methods of the technology (“methods”) comprise obtaining a suitable amount of certain rock volatile(s) that are capable of providing quantitative geologic unit pressure measurements (i.e., “pressure quantitative rock volatiles” (“PQRVs”)), quantifying such rock volatile(s) in geologic material(s) (GM(s)) in or from one or more specific locations (points, SLs, GULs, GUSLs) of a geologic unit (GU) and using such PQRV quantitative data (“PQRVQD”) (e.g., in certain aspects PQRVQD derivative data, PQRVQDDD) to quantify pressure in the GU in accordance with one or more quantitative analytical models (“QAM(s)”), which relate PQRV quantitative data (PQRVQD), directly, or relate a PQRVQD derivative (e.g., a ratio of PQRVQD to another rock volatile measure) to pressure of geologic material(s) in the geologic unit locations (aka/ac, GULs/SLs/GUSLs).

Readers are reminded that because this disclosure relates to many new and complex topics, extensive acronym use is employed herein to describe newly discovered objects/elements (e.g., PQRVQD). As the Summary notes, the section of this disclosure entitled “CONSTRUCTION PRINCIPLES AND DESCRIPTION OF SELECT TERMS” (infra) provides guidance concerning the interpretation of specialized terms and includes a list of commonly used acronyms of this disclosure. Even where a term is known in the art, that section, and other portions of this disclosure, may exemplify the term or describe different aspects/meanings/constructions of the term. For example, the term “model” is one where such different meanings can be associated with the term based on context, and where readers should ordinarily/often construe the term as implicitly disclosing different aspects of different scope(s).

Rock volatile data used in the methods of this disclosure (“methods”) is not limited in all aspects to solely PQRV data. For example, rock volatiles can serve as pressure-associated factor indicators (PMAFI(s)) (e.g., rock volatile water). Rock volatile derivatives (RVDs) also or alternatively can serve as PMAFI(s) (or be used in place of PQRVs). An RVD is a compound that is derived from a rock volatile (an RV), typically in the analytical process. For example, sulfur monoxide or other sulfate proxy can be formed as a molecular fragment of sulfate during analysis (e.g., during analysis by mass spectrometry) and thus is a derivative of a rock volatile (a rock volatile derivative, RVD) which is an indicator of the PMAFI sulfate, where each of the RVD sulfur monoxide/sulfate proxy and the PMAFI sulfate being an indicator of salinity/TDS. Note that herein, salinity and total dissolved solids are often presented together or, e.g., treated synonymously as, in the art, they are generally treated as representative of the same thing; it is, however, acknowledged that they are not in fact the same thing. Uncontradicted, disclosure related to or reference to one should be interpreted as disclosure also related to or referencing the other.

Accordingly, although rock volatiles are already extensively described in the art, including, particularly, in the RVS patents (see art cited in the Background and Construction and Terms sections of this disclosure), a brief description of the nature of rock volatiles is provided in this subsection for the convenience of readers and to illustrate certain aspects of methods.

RVs generally are understood to comprise compounds that are in a stable association with rock materials or other GMs but that are extractable from such materials upon application of gentle vacuum pressure or a suitable equivalent pressure/force. Such pressure(s) or alternative forces are described elsewhere herein or in the RVS patents. RVs are typically extractable as gases when sufficient and suitable gentle pressure is applied to the RV-associated geologic material. Each RV/volatile compound is a unique chemical of defined chemical composition. Common RVs are described in the RVS patents and include water, small linear and cyclic/aromatic hydrocarbons, organic acids, and other compounds found in subterranean environments, particularly in environments comprising geologic resources/reserves. Rock volatiles can mostly, generally only, or only be stably contained in pores or tight spaces of GMs/rocks, permitting extraction under gentle vacuum but otherwise maintaining the compound(s) in stable association with the rock of the applicable GM. Rock volatiles can, in aspects, also or alternatively contain some amount, e.g., a detectable or significant amount, of volatile(s) that is/are adsorbed on interior, unexposed surface(s) of the rock or rock material sample(s).

For clarity, the term “volatile” in “rock volatile” is alluding to the ability of the compound(s) that are characterizable as RV(s) to be extracted in suitably measurable amounts from GMs/rocks by application of gentle vacuum pressure or a gentle vacuum-equivalent force. RVs do not appreciably, significantly, or sizably volatize from rocks/GMs under ordinary conditions. Moreover, although RVs are typically extracted as gases, such a volatile compound may exist as a gas, liquid, or solid in a geologic material when exposed to standard atmospheric pressure and temperature. Rock volatiles are distinguishable from other rock-associated compounds that require extraction with significantly/sizably stronger/more destructive forces (such as forces that break down the crystalline structure of the material or that are required to disrupt physical structure(s) within the materials, such as fluid inclusions). In aspects, generally all, substantially all, essentially all, or all of the measured/analyzed volatile compound(s) do not originate from such sources. In aspects, most, generally all, substantially all, essentially all, or all of the volatile compound(s) originate from compounds associated with pores, tight spaces, and other locations in which “rock volatiles” are located as described in RVS patents.

As noted, RVs/volatiles are mostly, generally, substantially, or entirely in stable association with the rock material/geologic material from which the RV(s) originate, so long as the associated rock material remains in a relatively stable condition. The stable association means that RV(s) remain associated with the rock material/geologic material at the same, approximately the same, substantially the same, or significantly similar levels over significant periods of time, as exemplified in the Examples provided here and in the data presented in the RVS patents. Stable association alternatively can be characterized by the retention of suitable amounts of the RV(s) even over periods of time, even, in aspects, when the RV(s) have been exposed to ordinary environmental conditions (e.g., maintained in an unsealed/exposed state). A relatively stable condition in this case means geologic conditions under which the amount of RV(s) in a rock material remains mostly, substantially, generally, or entirely the same over time (e.g., over 5, 10, 15, 20, 25, 35, 50, or more years). In cases, however, RVs/volatiles in rock material(s) can be sizably, majorly, or significantly modified with respect to an earlier state of the applicable rock material. Determining such changes is one of the benefits and aspects of the technology provided here (e.g., in terms of pressure measurement-related information) and in other respects in the RVS patents. Such changes can arise from a variety of changes in the geologic environment that are described herein and in the RVS patents. Examples of such geologic changes include, e.g., the migration of geologic fluids (e.g., water, petroleum, gas) (e.g., due to the development or expansion of a fault or other conduit), human operations (e.g., drilling, extraction, etc.), or other changes that can impact the rock materials and change (typically reduce) the amount of RVs present in the rock materials. In cases, one or more rock volatiles may be affected by a particular condition change, while one or more other RVs/volatiles may not (it may also be possible that RVs are generated/lost due to changed conditions). In cases, the quantity or presence of certain RVs acts as an indicator or predictive factor for conditions or changes (as exemplified in the RVS patents). In the context of the present technology, the quantity of certain RVs (PQRVs) (e.g., carbon dioxide, or at least certain type(s) of carbon dioxide rock volatile(s)) is/are indicative of the fluid pressure state of the rock material (at present or over time).

The term “compound” is known to refer to a chemical substance defined by a unique chemical formula that is composed of two or more separate chemical elements. The term “compound” is sometimes used in place of terms such as “volatile” or “RV” herein. In aspects, most, generally all, or all of the RV molecules analyzed or utilized in a method are compounds. Readers will understand, however, that in aspects, RVs can comprise elements. In fact, certain elements (e.g., H₂ and He) can act as PQRVs. Accordingly, a term like RV/volatile can refer to either a compound, an elemental molecule, or an elemental atom. Moreover, uncontradicted any aspect that refers to a compound will be understood to also implicitly disclose corresponding aspects wherein the compound is substituted with an elemental atom, an elemental molecule, or both. In any such case, the “compound” refers to a discrete chemical. As noted, a compound is defined by a unique chemical formula. For example, an aqueous solution of sodium chloride is not a compound, whereas the sodium chloride and water are distinct compounds that are combined to form the aqueous sodium chloride solution. A compound can refer to an organic or inorganic compound. An “organic compound” is a compound in which one or more atoms of carbon are covalently linked to atoms of other elements, most commonly hydrogen, oxygen, nitrogen, and often phosphorus or sulfur, with the exclusion of certain carbon-containing compounds that in the art are not characterized as organic compounds (e.g., certain carbides, carbonates, and cyanides). An organic compound used in the methods herein can be a hydrocarbon (containing only carbon and hydrogen) and, in aspects, can be a saturated hydrocarbon (often referred to as an alkane) such as, e.g., butane, pentane, hexane, heptane, octane, nonane, decane, undecane, or dodecane or isomers thereof. In aspects, volatiles comprise, mostly comprise, generally consist of, or consist entirely of inorganic compounds. Organic compounds can lack carbon, include carbon-containing compounds not characterized as organic compounds in the art, or both. In aspects, volatiles comprise, mostly comprise, generally consist of, or consist entirely of inorganic compounds. Non-limiting examples of inorganic compounds/RVs that may be included method(s) include carbon dioxide (CO₂), carbonyl sulfide (OCS, also commonly abbreviated as COS), carbon disulfide (CS₂), sulfur dioxide (SO₂), hydrogen sulfide (H₂S), sulfate ion (SO₄)²⁻, or sulfur monoxide ion(s) such as, e.g., sulfur monoxide anion (SO). Another example of an inorganic compound is water. Uncontradicted, the term compound encompasses/implicitly discloses such organic and inorganic compounds and further encompasses what are defined as “related compound(s)” as defined herein. Uncontradicted, in certain aspects, reference to the comparison of a compound as a step of a method described herein (e.g., the comparison of a compound measured in a first sample to the same or related compound measured in a second or additional sample) can be interpreted as implicitly disclosing a comparison of data comprising data about the compound, such as a mathematical relationship involving the quantity of the compound, e.g., a ratio containing that compound. Uncontradicted, any specific disclosure of analyzing a compound provides support for analysis involving data comprising quantitative compound data (e.g., a ratio of the quantity of the compound to a quantity of another element, such as another RV). Compounds may exist as solids, liquids, and/or gases depending on the chemical and physical environment that they reside in and the temperature and pressure to which the compounds are subjected (e.g., condition(s) under which they are maintained). Certain compounds, such as but not limited to water and decane, may be condensable in a cryotrap, whereas others, such as but not limited to hydrogen, oxygen, and nitrogen, may not be condensable in a cryotrap. Uncontradicted, terms such as RV or volatile, etc., provide implicit disclosure of aspects in which the endogenous, extracted/measured, or quantified volatile(s) comprise or are or mostly are, generally consist of, or substantially consist of cryogenically condensable volatiles, cryogenically non-condensable volatiles, or both.

Uncontradicted, RVs are present in rocks, measured in or extracted from rock materials (e.g., measured in situ), and analyzed in suitable amounts. In aspects, the RVs are mostly, generally, or entirely extracted prior to analysis. In cases, it is not the RV that is actually analyzed in a method, but rather, a derivative of an RV (an RVD), which can also or alternatively be analyzed as part of the method. For example, certain RVs may be broken down into smaller compounds or form larger compounds in the performance of a method (e.g., in a mass spectrometry process). In cases, for example, a smaller compound derived from the breakdown of a larger compound may be analyzed. Uncontradicted, RVs analyzed in a method are present in suitable amounts at all relevant times of the method. The term “suitable amount” when applied to the quantity of RVs in a material, extracted from a material, or analyzed in a method generally means an amount/quantity that will provide a reliable/accurate indication of the quantity of the analyzed RVs. The concept of “suitability” with respect to the technology is further described elsewhere. Briefly, suitable amounts of material can vary according to how the volatile compounds are removed from the material and the technology used to measure such compounds. Suitable amounts of materials can also vary according to concentration and also according to how easy or hard it is to extract the volatile compounds from the material (e.g., with material characteristics such as porosity). The level of difficulty to extract the volatile compounds can depend on the vapor pressure of the volatile compounds, the porosity of the material, the size of solids within the material, or how tightly the volatile compounds are associated with the material, among other considerations (e.g., considerations known in the art, described in the RVS patents, or described in the art in general).

PQRVs: Pressure Quantitative Rock Volatiles

As described elsewhere, a facet of the invention is the use of pressure quantitative rock volatile (PQRV) in the quantification of geologic unit-specific location fluid pressure (and, collectively, geologic unit pressure). The quantification of geologic unit (GU) or geologic unit specific location (GUSL) fluid pressure in methods provided here is often performed in accordance with one or more quantitative analytical models (QAM(s) or, sometimes aka “models”) that at least reliably relate PQRV quantitative data (PQRVQD), either alone/directly, or relate PQRVQD derivative data (PQRVQDDD) (e.g., a ratio of PQRVQD to another rock volatile measurement/data measure, such as pressure measurement adjusting factor quantitative data (PMAFQD)) to the fluid pressure of geologic material(s) in the GU specific locations (GUSLs). As discussed elsewhere, readers will recognize that the “pressure” measured by PQRV quantitation data (QD) in methods typically refers to fluid pressure(s) measured at the various geologic unit-specific locations (GUSLs/GLs/SLs) analyzed by the method (i.e., the pressure exerted by the fluid(s) of the GUSLs). Geologic pressure typically comprises both lithostatic pressure (overburden stress pressure) and hydrostatic pressure (pressure caused by fluids in the geologic unit, e.g., pore fluid(s)). Uncontradicted, pressure herein comprises, mostly comprises, generally consists of, substantially consists of, or only consists of fluid pressure/hydrostatic pressure. The PQRV(s) quantified in such method(s) can be any suitable rock volatile(s) which are demonstrated to provide an at least reliable corresponding specific location (SL/GUSL) pressure quantification when the associated PQRV quantitative data (PQRVQD) is measured in accordance with a suitable quantitative analytical model (QAM) that at least reliably relates the PQRVQD to GU specific location pressure in a repeatable manner in at least certain conditions/cases.

Standards of reliability for models/QAM(s) are discussed and exemplified elsewhere (see, e.g., CONSTRUCTION AND TERMS). In general, a “reliable” model accurately measures pressure at least about 70% or ≥˜75% of the time (at least in contexts that the practitioner deems to be comparable to the context being evaluated) and a highly reliable model is a model that accurately measures pressure at least about 85% of the time (e.g., ≥˜90%, ˜92.5%, ˜95%, ˜96.5%, ˜98%, ˜99%, or at least ˜99.5% of the time in the applicable context(s)). As noted, methods can comprise an evaluation of “context.” Context means the various conditions that can impact the suitability of an analysis—e.g., the GM analyzed, conditions of the GU, the fit of input data (PQRVQD/PQRVQDDD) to model(s), etc. Factors that impact context include alternative conditions (ACs) and pressure measurement adjusting factors (PMAFs), which are discussed elsewhere. Steps of methods can comprise the evaluation of context to determine if a model is applicable to test data or to provide indicators of what adjustments to a model can lead to reliability between input data and DPM(s). In aspects, context is considered before, after, or both before and after evaluating a fit between input data and a model. In aspects where a reliable fit is not made, a model is adjusted, a data set is adjusted, or a new model is generated and then evaluated, etc.

It can be important to understand that a PQRV may not act as a reliable measure of GU/GU-specific location pressure in all cases/contexts. In other words, 100% effectiveness is not a necessary requirement/characteristic of a rock volatile in order for the volatile to qualify as a PQRV. In fact, a PQRV may only be effective in a limited number of contexts. In some cases, a PQRV may not be able to act as a measure of GU pressure, either overall in the given context or with the currently available models in the given context. In cases, this is because other factors may significantly influence the ability of a PQRV and act as an effective measure of geologic unit-specific location pressure where such factors are present/at work. Such factors can include pressure measurement adjusting factors (PMAFs), which are discussed further elsewhere.

In aspects, PQRVQD-derivative data is generated by generating a relationship between PQRVQD and pressure measurement affecting factor (PMAF) quantitative data (QD) (i.e., PMAFQD). PMAFQD can be quantitative data regarding a present pressure measurement adjusting factor (PMAF—e.g., salinity, presence of water, presence of a structural feature such as a fault, biogenic activity, temperature, pH, etc.) or an indicator of a PMAF (e.g., an RV that is indicative of the presence of a PMAF). The PMAF can either be a factor that directly impacts/modifies geologic unit (GU) pressure or that impacts the quantity or quantification of PQRV(s). For example, salinity may impact the solubility of RVs in GU fluids and, accordingly, may impact the quantity or quantification of such RVs in geologic materials.

In cases, the PMAFQD is a direct measurement that can be directly input into a model (e.g., a quantity of an RV that serves as a PMAF indicator (PMAFI)). In cases, the model comprises a value that has been assigned to a PMAF that can be input into the applicable model (as PMAFQD, or which may be referred to herein as PMAFIQD) to achieve at least reliable pressure measurements in the given context. For example, a value may be assigned to the existence of a structural feature, temperature, or pH of a specific location, and that value is input into a model.

Given such elements of the technology, readers should understand that, at least in the broadest sense, the term pressure quantitative rock volatile (PQRV) includes rock volatiles that act as PQRVs only when the data for the quantity of such RV (PQRVQD) is combined with or put in relationship with pressure measurement affecting factor quantitative data (PMAFQD) in a model or other relationship (e.g., a ratio, such as a 1:1 ratio). In aspects, some, most, generally all, substantially all, or all of the PQRV-related data input into a model is presented in relation to PMAFQD, e.g., the PQRVQD and PMAFQD may be presented in a ratio. For example, by factoring in a measure of water (e.g., using RV water as a PMAFI), it is possible to provide a measure of the concentration of a PQRV, which, in aspects, can provide a detectably, sizably, or significantly improved pressure measurement when input into a model, as opposed to the PQRVQD alone. However, in other aspects, the PQRVs of a method are mostly, generally, substantially, or entirely limited to rock volatiles that, at least in some contexts, can independently be used in a model (apart from any PMAFQD input(s)) to reliably predict geologic unit-specific location pressure (GUSLP).

It may also be the case that a measured RV that acts as a PQRV is actually an indicator of the presence of some other PQRV. In other words, a derivative of a PQRV or a proxy of a PQRD that still functionally serves as a PQRV in the given quantitative/analytical technologies used in performing the method can still, at least in one aspect, be characterized as a PQRV. For example, in using mass spectrometry analytical methods, it is possible that certain RVs (e.g., ionic RVs) may be broken down into smaller compounds and such smaller compounds determined to be capable of acting as PQRVs when measured (e.g., a smaller ionic fragment of the parent), even though they may have originated from a larger “parent” RV. In certain aspects, one or more smaller compounds, or, also or alternatively, new chemical(s) or compound(s) formed during process(es) of method(s), such as, e.g., the ionization of RVS method(s) described herein, sometimes referenced as “adducts” in literature of relevant art, may be capable of acting as PQRVs, and should be considered as incorporated into the scope of PQRVs described herein.

In aspects, PQRVs are quantified mostly, generally, substantially, or entirely using rock volatile stratigraphy (RVS) technology, e.g., as described in the RVS patents or elsewhere in the art (e.g., comprising extraction of the rock volatiles by one, two, or more types of gentle vacuum pressure extraction). In certain aspects, pressure quantitative rock volatile(s) are volatile(s) which are easily extractable, as in, e.g., extractable from rock material sample(s) by application of a relatively weak/gentle vacuum pressure (e.g., a vacuum pressure/suction of about 200 millibars or greater, such as to near atmospheric pressure). In aspects, quantitative rock volatile(s) is/are mostly, generally, or only composed of easily extractable rock volatile(s).

The developer of the technology has already identified a number of PQRVs, and the developer expects that skilled artisans, using the principles provided here, may be able to identify a number of additional rock volatiles with respect to the list of known PQRVs using the methods described herein or equivalent steps/means likely without undue experimentation.

In one aspect, a PQRV or the PQRV used in methods comprises, mostly is, generally is, or consists of hydrogen, typically hydrogen gas (H₂). In aspects, the hydrogen PQRV is measured by cryogenic trap-and-release mass spectrometry/RVS, as described in the RVS patents and related art.

In an aspect, a PQRV or the PQRV comprises, mostly is, generally is, or consists of hydrogen sulfide (H₂S). However, the developer recognizes that an RV should be generally suitable in the context it is used, and for a number of reasons, hydrogen sulfide is often not suitable. Such a lack of suitability can arise from the fact that the presence of hydrogen sulfide may represent or at least indicate hazardous subsurface conditions. Additionally, H₂S is often highly reactive and, for this and possibly other reasons, not regularly present. In aspects, the PQRV used in a method presents a risk of hazard that is significantly or sizably less than that presented by hydrogen sulfide. In aspects, the PQRV(s) used in the method are found with greater frequency, in greater quantity, or both, than hydrogen sulfide.

In another aspect, a PQRV or the PQRV used in methods comprises, mostly is, generally is, or consists of helium (He). In aspects, the helium PQRV is measured by cryogenic trap-and-release mass spectrometry/RVS, as described in the RVS patents and related art.

In a further aspect, a PQRV or the PQRV used in methods comprises, mostly is, generally is, or consists of a water-soluble hydrocarbon, e.g., a small (C1-C3 or C1-C2) alkane hydrocarbon, or methane, and, in aspects, mostly, generally, substantially, or only methane. In aspects, the suitable hydrocarbon/methane PQRV is measured by cryogenic trap-and-release mass spectrometry/RVS, as described in the RVS patents and related art.

In another aspect, it may be possible that suitable inorganic carbon acids (e.g., carbonic acid) or bicarbonate, each of which is classified as CDRCs in the RVS patents, can act as PQRV(s) in methods. Accordingly, in aspects, one or both of such compounds can be considered as PQRVs/used as PQRVs in methods. However, such CDRCs often decompose quickly (to carbon dioxide or carbon dioxide and water). Accordingly, in aspects, such potential PQRVs are only available in amounts that are not suitable. In aspects, PQRVs are characterizable as compounds that are mostly, generally, or substantially more available in the applicable rock materials or most rock materials as RVs than carbonic acid, bicarbonate, or both. In aspects, either or both of such compounds are not used as PQRVs for these and possibly other reasons.

Other CDRCs described in the RVS patents are, in aspects, surprisingly unsuitable for use as PQRVs. For example, the developer has determined that both formic acid and acetic acid are not suitable as PQRVs and, accordingly, at least in aspects are excluded from use as PQRVs in methods. However, such and other organic acids, as discussed elsewhere, can be used as pressure measurement adjusting factor indicators (PMAFIs), specifically in that, i.a., such organic acids are likely indicators of biogenic activity in the applicable geologic unit, which can impact PQRV pressure measurement for one or more reasons.

In aspects, the pressure quantitative rock volatile (PQRV) is rock volatile carbon dioxide (aka, “CO₂” or “CO2”—and in certain larger acronyms “CD”). In certain aspects, quantitative rock volatile(s) quantified in method(s) disclosed herein are mostly, generally, or only composed of rock volatile carbon dioxide or of type(s) of rock volatile carbon dioxide. In aspects, one or more pressure quantitative rock volatile(s) associated with model development (a putative analytical model (PAM)), or a validated model (a QAM/EQAM), comprises rock volatile CO₂, or is mostly, generally, or only composed of rock volatile carbon dioxide, such as, e.g., easily extractable rock volatile carbon dioxide.

In exemplary aspects, the PQRV used in methods comprises, generally is, or entirely is composed of rock volatile carbon dioxide (CO₂/CD) that is extractable by applying gentle vacuum at a pressure of ≥6 millibars, e.g., ≥10 millibars, such as about 10-about 200 millibars, such as about 10 to about 100 millibars, e.g., about 10-about 80 millibars or about 10-about 60 millibars, for a period of about 5-30 minutes, e.g., about 5-20 minutes, such as about 7.5-15 minutes, such as about 10 minutes. In particular aspects, methods are performed using easily extractable rock volatile carbon dioxide (EERVCD) as a/the PQRV of the method. In certain aspects, methods are performed using easily extractable rock volatile carbon dioxide (EERVCD) as a/the PQRV of the method, wherein the EERVCD is extracted using a pressure of about 20 millibars for a period of about 5-30 minutes.

In aspects, release resistant rock volatile carbon dioxide (RRRVCD) is a superior PQRV than EERVCD and is alternatively used as the/a PQRV. In aspects, RRRVCD is RVCD extracted using a stronger but still gentle vacuum pressure, e.g., a pressure closer to about, for example, 2 millibars. See additional description(s) of vacuum pressure(s) provided elsewhere herein. In still alternative aspects, the sum of RRRVCD and EERVCD is a suitable or superior PQRV than either RRRVCD or EERVCD alone (or is selected for use in method(s)). As described below and as exemplified in the Figures, in certain aspects a method can comprise the step of determining (1) if EERVCD is or is not an effective PQRV in the applicable context/conditions of the method (either on its own or after application of various methods to improve the PQRV data that is input in the model, possibly including relating the PQRV quantitative data to PMAFQD (e.g., by a ratio that reflects concentration of the PQRV) or (2) if the EERVCD is not an effective/suitable PQRV attempting to use the RRRVCD QD or RRRVCD-derived data in a model, either alone or in combination with EERVCD (e.g., as a sum of EERVCDQD and RRRVCDQD). Any procedure described in the Figures (in this context or otherwise) can be modified by the addition, deletion, or reordering of 1, 2, or 3 steps, or about 10%, 20%, or about 25% of the steps of the illustrated method (e.g., where associated condition data (ACD) indicates to the practitioner that RRRVCDQD should be first analyzed instead of EERVCDQD, the method is modified by skipping/omitting the step of analyzing EERVCDQD and some of the steps directed to improving an EERVCDQD model may then be applied to developing a RRRVCDQD model; similarly, if the practitioner believes that application/incorporation of the sum of RRRVCDQD and EERVCDQD in a model would be suitable, the method(s) herein can be modified by skipping/omitting step(s) for analyzing EERVCDQD, RRRVCDQD, or both independently). Readers will also understand that this principle of testing a first candidate PQRV, then a second PQRV, etc., as illustrated in some of the procedure Figures provided here (such testing including modification of data/model (e.g., using PQRVQD-derived data, selectively removing PQRVQD from a larger PQRVQD data set, etc.)), is a general aspect of the technology, which may be applicable to any number and type of known or potential PQRVs. Similarly, Readers will understand that, in certain aspects, reference to easily extractable rock volatile data can, where ACD or practitioner knowledge/experience dictate it would be appropriate, be substituted with release-resistant rock volatile data or the sum of easily extractable and release-resistant rock volatile data in the method(s) described herein. Likewise, reference to release resistant rock volatile data can, where ACD or practitioner knowledge/experience dictate it would be appropriate, be substituted with easily extractable rock volatile data or the sum of easily extractable and release resistant rock volatile data (aka, total rock volatile data—for any one or more of the volatiles described herein) in method(s) described herein. Again, likewise, reference to the sum of release resistant rock volatile data and easily extractable rock volatile data can, where ACD or practitioner knowledge/experience dictate it would be appropriate, be substituted with easily extractable rock volatile data or release resistant rock volatile data in the method(s) described herein. Still further, Readers will understand that in certain aspects, reference to PQRVQD (e.g., easily extractable PQRVQD, release-resistant PQRVQD, or easily and release-resistant sum PQRVQD) can be provided in model(s), in aspects, with or without one or more PMAFs (as described elsewhere herein).

Geologic Materials and Samples

Rock volatiles (RVs) used in methods of the technology or otherwise can be classified as or are contained in/obtained from “geologic materials” (GMs).

Uncontradicted, geologic materials in this regard, when applied to methods, typically mean those GMs that contain a suitably measurable amount of RVs, permitting the analysis of pressure or other geologic unit state according to methods of the technology. In aspects, GMs comprise little rock materials but contain RVs (e.g., a GM may be a material that has been in contact with RVs and taken on a suitably measurable amount of RVs). In aspects, GMs comprise some amount of rock materials (rocks in natural form, or, more likely, materials composed from the breakup of rock—e.g., drill cuttings). In aspects, GMs mostly comprise, generally consist of, substantially consist of, consist essentially of, or consist entirely of rock (aka/ac, “rock materials).” Geologic materials can include materials made from human activities, but that comprise geologic materials, such as rock materials. Examples of such materials include core samples and drilling muds. In aspects, such materials are used in the methods of the technology. In aspects, most, generally all, or all of the GMs analyzed in methods are samples that in some manner provide an indication of the GU, zone, site, etc., from which the GMs originate. Means/steps for determining such locations are known. E.g., timing of delivery of cuttings to the surface, known position of a well, or similar type of information can provide an indication of the depth of a borehole from which the cuttings originate.

The GM/rock materials (GM(s)/RM(s)) used in the methods of the technology can be any suitable type of material in any suitable location. As described in the RVS patents, in situ measurement of RVs may be possible under certain conditions using known technology (e.g., methods described in the RVS patents or equivalents thereof). Accordingly, in situ measurements for RVs are, at least in a broad aspect, within the scope of the methods of the technology.

In other aspects, rock material sample(s) used in methods are rock material/geologic material sample(s) collected from different specific locations (SLs, GUSLs) of a geologic unit or of a collection of geologic units (e.g., different fields in a play, basin, etc.).

Geologic units are extensively described in the CONSTRUCTION AND TERMS section of this disclosure, the RVS patents, and in the art. Briefly, a geologic unit (GU) can comprise, e.g., a prospective petroleum production site, or, e.g., a petroleum production site (e.g., an active petroleum site). In certain aspects, a geologic unit comprises a carbon sequestration site. In certain aspects, a geologic unit can comprise a region comprising two or more fields, each field comprising a plurality of prospective sites, production sites, or both. Further, in aspects, a geologic unit can comprise a tight rock area or a tight formation. In certain aspects, a geologic unit comprises one or more horizontal wells/boreholes associated with, e.g., a tight rock area or tight formation. Geologic units can overlap in definition. Disclosure relating to a GU thus provides implicit support for corresponding aspects involving GUs or portion(s) of GU(s).

The rock material/geologic material samples used in methods involving samples can be any suitable type of sample or any suitable type of material, collected in any suitable type of manner. In aspects, for example, rock material/geologic material samples used in method(s) involving sample(s) herein can be, e.g., core sample(s) or, e.g., drill cutting(s) sample(s), or both. In aspects, the rock material sample used in a method is mostly, generally, substantially, or entirely composed of drill cuttings. Drill cuttings are frequently discussed herein. Uncontradicted, any aspect directed to drill cuttings, or another specific type of GM/rock material, can be substituted with any other type disclosed herein in implicit alternative aspects. Briefly, drill cuttings are rock fragments/materials that are brought to the surface in a drilling operation (such terms are generally understood in the art). Typically, drill cuttings are rocks that are maintained separated from drill mud(s) in a shaker table operation or similar separation process. Drill cuttings can be of any suitable size and be made by any suitable technique. In aspects suitable drill cuttings comprise, mostly, generally, or entirely are PDC cuttings (as described in the RVS patents) or cuttings of similar or even smaller size. The size of cuttings produced at a well, borehole, or borehole environment will depend on several factors, including the geologic material being drilled through and the drill bit used, with more modern drill bits, such as polycrystalline drill bits, often forming smaller cuttings. Particle sizes of cuttings can be, for example, as small as about 5 microns (e.g., about 10 microns or larger, about 20 microns or larger, about 25 microns or larger, about 50 microns or larger, etc.), but typically the cuttings will have particle sizes of at least about 100 microns, such as at least about 150 microns, or at least about 200 microns (e.g., about 250 microns or greater), and may be significantly larger, such as up to about 7.5 mm (e.g., about 6.5 mm or less, about 6 mm or less, or ≤˜5 mm).

Uncontradicted, a sample/material can have any suitable form and be in any suitable amount of material and condition. Uncontradicted, a suitable amount of a sample is an amount that comprises at least a measurable amount of extractable RV compounds (when subjected to suitable RV extraction methods). While a sample will contain some amount of rock material, a sample may mostly/mainly contain or generally contain solid or liquid material. Cuttings and similar samples, e.g., rock material samples including core samples, will have a composition that reflects the composition of the borehole, which will vary in accordance with the compositional characteristics of the formations and zones that the borehole traverses or abuts. In this and other respects, most, generally all, substantially all, or all of the tested samples may be homogeneous or heterogeneous (in terms of size/weight, composition, or some combination thereof). In aspects, the overall collection of rock material sample(s) used in method(s) herein can comprise a plurality of rock types, such as, e.g., at least about 2, ˜3, ˜4, or ˜5 or more rock types. Exemplary rock types that may make up some, if not most, or generally all, substantially all, or all of a collection/population of samples comprise, e.g., sandstones, limestones, and dolomites.

In aspects, rock material samples are sealed, e.g., hermetically sealed, usually promptly following collection, e.g., within less than about one day, less than about 4 hours, less than about 1 hour, less than about 20 minutes, less than about 10 minutes, less than about 5 minutes, less than about 1 minute, or less than 15 seconds, to avoid loss of RV compounds. However, in other aspects, the rock material samples analyzed in a method are mostly, generally, substantially, or entirely not hermetically sealed after collection. In aspects, as exemplified in the Examples, rock material samples may be stored in uncontrolled environmental conditions, e.g., a non-climate-controlled warehouse, for 5, 10, 15, 25, 35, 50, 65, or 80 years or more until such materials are used in the methods described herein.

In aspects, methods comprise obtaining one or more rock material sample(s), such as, e.g., drill cuttings sample(s), from separated specific locations (e.g., locations that vary by, on average, in most cases, in generally call cases, in substantially all cases, or in all cases, ˜150 ft. or less, ˜100 ft or less, such as ˜90 ft, ˜75 ft, ˜70 ft, ˜60 ft, ˜50 ft, ˜40 ft, ˜35 ft, ˜30 ft, ˜25 ft, ˜20 ft, ˜15 ft, ˜10 ft, or ˜5 ft, ˜3 ft or less (e.g., ˜5-250 ft., 5-200 ft., 5-300 ft., 5-150 ft., 5-100 ft. 10-250 ft., 10-200 ft., 10-150 ft., 10-100 ft., 15-300 ft., 15-225 ft., or ˜15-150 ft.). In aspects, materials or measurements are made in at least about 10, e.g., >˜20, >˜40, >˜50, >˜75, >˜100, or more specific locations (e.g., ˜5-1000, 10-1000, 10-750, 15-750, 20-1000, 50-1000, 100-1000, 200-1000, 50-500, 50-750, 100-750, 100-500, 100-2000, or ˜100-1500 specific locations).

As noted, samples may also be of GM that contains rock materials. One example of such a material is drilling mud. Drilling muds are typically classified as either water-based mud (WBM) or oil-based mud (OBM). WBMs are typically a homogeneous blend of water and one or more clays, such as bentonite, and often include other performance-enhancing chemicals (e.g., potassium formate). OBM is usually an emulsion composed primarily of an oil-based continuous phase (comprising diesel, kerosene, fuel oil, mineral oil, or even crude oil) and an aqueous dispersed phase, which may optionally contain emulsifiers, wetting agents, or gelling agents. Oil-based muds have their own hydrocarbon signatures, and often such signatures interfere with known analytical methods. Uncontradicted, methods of the invention can be practiced using any suitable type of mud.

Another possible type of sample for use in the methods of the technology is a core sample. Core samples are commonly generated in oil exploration and related processes and are well understood in the art. In aspects, core samples or samples of a core sample are used as a source of rock volatiles in the performance of the method, alone or with measurements of rock volatiles from other locations or other materials (e.g., drill cuttings). In certain aspects, specific types of core sample(s) are suitable for use in method(s) herein. In certain aspects, core sample(s) flushed to a detectably or significantly greater extent than a conventional core sample, or, e.g., a diamond core sample, may be suitable for use in method(s) herein. In certain aspects, core sample(s) such as side wall core sample(s) are suitable for use in method(s) herein. Where, e.g., drill cutting(s) are specifically described herein, one may also or alternatively consider the use of core sample(s).

Quantitation of Fluid Pressure Through PQRVQD/PQRVQDDD

Aspects of this technology comprise generating quantitative pressure measurements at one or more specific locations in a geologic unit, e.g., borehole environment(s), based on, i.a., PQRVQD or PQRVQDDD. As described elsewhere, such methods can comprise (1) comparing PQRVQD/PQRVQDDD to (a) a model/QAM (comparison to a model can comprise, e.g., comparison to data points from a validated model) (e.g., to determine if there is a strong/reliable fit) and (2) in accordance with the model determining the GU liquid pressure measurement.

In aspects, a method can comprise simply determining if there is a fit among PQRVQD/PQRVQDDD, where there are sufficient contextual factors to indicate that the PQRVQD/PQRVQDDD is indicative of a liquid pressure measurement consistent with a known model. Such a method can comprise (1) determining that PQRVQD is consistent with a context where a validated PQRVQD/PQRVQDDD data set/QAM has been established and (2) quantifying the GU liquid pressure based on applying the PQRVQD/PQRVQDDD to the model. Contextual factors can include, e.g., a similar determination of GU liquid pressure based on similar GM, GU, PQRVQD/PQRVQDDD, or similar ACD, etc. (e.g., 2, 3, or 4 thereof).

Pressure measurement (geologic unit liquid measurements/hydrostatic GU measurements) herein typically generally, substantially, or only represents the pressure of liquids at specific location(s) of a GU. Pressure measurements can be expressed in any suitable unit of measurement (e.g., in pounds per square inch (psi)).

In aspects, the determination of GU fluid pressure comprises, consists of, is mostly based on, or is generally based on, raw PQRVQD measures that are input into a model that provides reliable or highly reliable fluid pressure measurements for most, generally all, substantially all, or all of the SLs from which PQRVQD was obtained within the GU. Such PQRVQD measures may comprise most, generally all, or all of an original PQRVQD data set for the tested portion of a GU or the GU. In other aspects, the PQRVQD data that is input to the quantitative analytical model to measure GU fluid pressures is a modified data set, typically a data set from which one or more PQRVQD data points have been removed. As exemplified herein, excluded PQRVQD data points/data might be excluded on the basis of different associated conditions/associated condition data (ACD)/pressure measurement affecting factors (PMAF(s)) (e.g., reflecting known differences in a GU, such as relatively low salinity and relatively high salinity wells in a field or play). As is described elsewhere, the establishment of QAM(s)/EQAM(s) can also comprise, consist of, be mostly based on, or be generally based on, raw PQRVQD measures to provide reliable or highly reliable fluid pressure measurements for some, most, generally all, substantially all, or all of the SLs from which geological material sample data is obtained; however, QAM(s)/EQAM(s) can also account for ACD by the exclusion of relevant input(s) to the model based on ACD as identified as appropriate by method(s) herein.

In aspects, most, generally all, or all of the PQRVQD-related data input to a quantitative analytical model (QAM) of a method is PQRVQD-derived data (PQRVQDDD). In aspects, most, generally all, substantially all, or all of the PQRVQD-derived data is data based on a relationship between the PQRVQD and quantitative pressure measurement adjusting factor (PMAF) data or quantitative pressure measurement adjusting factor indicator (PMAFI) data (either being classified here as PMAFQD). PMAFs, PMAFIs, and PMAFQD are discussed elsewhere (see, e.g., the subsection entitled FACTORS IMPACTING PRESSURE MEASUREMENT OR MODEL USE). In aspects, the relationship is a ratio. In aspects, the ratio is between PQRVQD and PMAFQD, in which the PMAFQD reflects data from a PMAF or PMAFI that reflects the concentration of the PQRV in the GU (e.g., the PMAFI is one or more types of rock volatile water, such as EEW or sum/total EW/RV water).

In aspects, GU fluid pressure is measured in accordance with a model (a QAM or EQAM) that factors in at least one additional (secondary) type of PMAFQDs, where secondary PMAFQD(s) is/are also measured in the GU, usually at or about/around the specific locations and usually within the same material or type of material as the PQRVQD is generated from. Herein, as described elsewhere, “about” a location means an area that is about the same specific location, meaning a location which provides a sizably similar or significantly similar result(s) as the specific location, is within a zone that is less than 1.5× the size of the space/zone that defines the specific location, or that varies from the zone/area of the specific location by less than 33% of the distance between the specific location and the nearest specific location or less than 33% of the average distance between specific locations. In aspects, the secondary PMAF/PAMFI associated with the secondary PMAFQD is associated with the salinity of the GU. In other aspects, secondary PMAF/PAMFI associated with the secondary PMAFQD is associated with another PMAF—e.g., temperature, total dissolved solids (TDS), biogenic activity, or pH, etc.

In aspects, suitable technique(s) comprise the use of rock volatile stratigraphy (RVS) method(s) previously disclosed and described (at least in brief) herein. In certain aspects, extraction method(s) comprise, e.g., extraction of rock volatile(s) by gentle/relatively weak vacuum, e.g., vacuum pressure(s) of >˜20 mbar (e.g., ˜5 millibar to about 1 atmosphere, ˜10, ˜12.5, or ˜15 millibars-˜1 atmosphere) or e.g., other pressure(s) associated with gentle vacuum extraction as described elsewhere herein, such as those associated with RVS method(s). Gentle extraction can be performed at any suitable vacuum strength applied under any suitable conditions. In aspects, the gentle vacuum applied in the method comprises a gentle vacuum force/is characterizable as a low extraction force gentle vacuum. In aspects, a gentle vacuum comprises application of a vacuum force that is close to atmospheric pressure (e.g., >˜100, >˜200, >˜300, >˜400, >˜500, or >˜700 millibars but less than atmospheric pressure (1013.25 millibars)). In aspects, the gentle vacuum applied in the method also or alternatively comprises a high extraction force gentle vacuum. Relevant description of such terminology is provided in the CONSTRUCTION AND TERMS section of this disclosure, and suitable gentle vacuum methods and characteristics are further described in the RVS patents. Other gentle vacuum conditions may be suitable so long as the gentle vacuum conditions are able to extract a suitable quantity of the RVs of interest (e.g., carbon dioxide) under desired conditions, provided that the application of the gentle vacuum force does not render the amount of RVs unsuitable for measurement (or, in aspects, sizably or significantly reduce the number of RVs available for analysis by the applied method(s)). Gentle vacuum equivalent force(s) also or alternatively can be employed in the extraction of RVs from rock materials. Such equivalent forces are described in the RVS patents and in the art. In aspects, RV compounds are extracted by application of two or more different discrete vacuum pressures applied at two separate times, wherein each of the RV aliquots collected by such pressure applications is separately analyzed by a suitable analytical method (e.g., the RV compounds can be analyzed by two aliquot methods as exemplified in several of the RVS patents). In one aspect, gentle vacuum conditions that can be applied in 1, 2, 3, or 4 aliquot methods mean application of a pressure of less than 200 millibars, such as a first phase about 100 millibars, or for between about 1 and 100 millibars, such as for example between about 5 and 90 millibars, or for example between about 10 and 80 millibars, or between about 20 and 70 millibars, or between about 30 and 60 millibars, or for example between about 40 and 50 millibars. Such a first phase can, in some aspects, be followed by another one or more phases of a different pressure, such as a pressure less than 100 millibars, such as for example less than about 100 millibars, less than about 90 millibars, less than about 80 millibars, less than about 70 millibars, less than about 60 millibars, less than about 50 millibars, less than about 40 millibars, less than about 30 millibars, less than about 20 millibars, less than about 10 millibars, less than about 5 millibars, or even less than 1 millibar, such as less than about 0.5 millibars, less than about 0.1 millibars, less than about 0.01 millibars, less than about 0.001 millibars, or even less than about 0.0001 millibars, the extraction extracting volatile gas and fluid species. In some embodiments, such extraction is followed by cryogenic trapping of selected compounds and the subsequent controlled, slow release thereof. In aspects, such 1, 2, 3, or more aliquot extraction methods can comprise application of one or more vacuums with a pressure of about 1×10-2 millibars or less at room temperature applied for about 3-30, 4-24, 5-20, or 5-15 minutes.

In facets method(s) of quantitative analysis of one or more extracted rock volatile(s) comprise isolating RVs prior to analysis. In aspects, methods comprise isolating and concentrating RVs (e.g., trapping RVs). In aspects, RVs are collected in two or more separate collections before analysis (e.g., where cryogenic trapping is used, methods can comprise using the cryogenic trap to collect cryotrap-condensable volatiles and separately collecting non-condensable volatiles that do not bind to the cryotrap(s)). In aspects, RVs analyzed in the method are mostly, generally, or entirely extracted and quantified using rock volatile stratigraphy (RVS) methods (typically comprising, i.a., cryogenic trap-and-release mass spectrometry quantitative analysis of rock volatiles).

According to aspects, one or more pressure quantitative rock volatiles measured in methods comprise rock volatile CO₂ (RVCD), or is mostly, generally, or only composed of rock volatile carbon dioxide. In aspects, the RVCD is mostly, generally, or entirely one type of RVCD. In aspects, the RVCD is mostly, generally, or only composed of easily extractable rock volatile carbon dioxide. In other aspects, the RVCD is mostly, generally, or only composed of release-resistant rock volatile carbon dioxide. In aspects, a sum or ratio of the quantities of two or more types of carbon dioxide rock volatiles is used in methods.

In aspects, method(s) of determining the pressure within at least a portion of a geologic unit, e.g., method(s) of determining pressure at one or more specific locations, e.g., one or more specific location(s) within at least a portion of a geologic unit comprise the use of rock material sample(s) wherein at least one rock material sample comprises a plurality of rock types, such as, e.g., at least about 2, ˜3, ˜4, or ˜5 or more rock types. In certain aspects, such rock types can be, e.g., sandstone, limestone, or dolomite type(s). In certain aspects, such rock types can be, e.g., one or more rock types not characterizable as one or more of sandstone, limestone, or dolomites.

Any suitable number of PQRV quantities from any suitable number of specific locations and geologic materials can be used in methods. In certain aspects, rock material from which PQRVs are measured or obtained and measured in method(s) comprises at least two sandstones, limestones, and dolomites.

In aspects, methods comprise the analysis of a plurality of locations and optionally further a plurality of different geologic materials in different specific locations of a geologic unit. In aspects, methods of the invention comprise providing at least 10, 20, 30, 40, 50, 100, or more, e.g., 5-500, 5-250, 10-200, or 15-300 PQRVQD-based/derived pressure measurements for a corresponding number or approximately corresponding number of specific locations within the at least portion of at least one geologic unit, which pressure measurements are in at least part, based on the quantity of the one or more pressure quantitative rock volatiles.

Quantitation of RVs in situ or extracted can be performed by any suitable analytical method. As mentioned elsewhere, one such method commonly used in RVS is mass spectrometry analysis (e.g., using any of the mass spectrometry tools and techniques exemplified in the RVS patents). Other methods of analyzing volatiles are known and described/mentioned in the RVS patents or are known in the art. Such other methods can include, e.g., gas chromatography analytical methods. Methods can comprise the use of such alternate methods in addition to or as an alternative to mass spectrometry quantification of RVs. In aspects, RV quantification is mostly, generally, or only carried out through mass spectrometry analysis.

As noted elsewhere, in this and other facets of the technology (ies), element(s)/step(s) of the technology (ies) also can be characterized as comprising a “means” for providing a recited function or, e.g., a “means” for performing, participating in, or achieving particular step(s) of method(s) (aka/ac, as applicable, a “step for” carrying out a function/activity in a method) (see the CONSTRUCTION AND TERMS section for further illustration and discussion). Means for/steps for both the quantitation of rock volatiles and for the extraction of rock volatiles from rock materials are exemplified in the RVS patents and elsewhere herein. In such respect, any known or identified equivalents of the named elements/steps provided in this subsection or elsewhere here can be substituted for any of the steps/elements explicitly mentioned here. As with other sections similarly described herein, any of the facets of the described technology (ies) can be, where suitable, described alternatively as steps for or means for carrying out associated functions (e.g., the above-described elements/steps). The function of any such elements/steps need not be explicitly stated herein where it is clear to readers based on context as provided here or by knowledge.

According to aspects, certain volatile compound(s) (PQRVs) correlate with pressure (aka/ac “pressure-indicative compounds”). As noted, PQRVs can include, e.g., carbon dioxide. In aspects, methods also or alternatively comprise comparing quantities of one or more pressure-indicative compounds (in different locations, at different times, etc.), either as raw/unmodified PQRV data, or as modified (e.g., adjusted based on factoring in water content—e.g., as rock volatile water quantity). Such comparative measures of methods can provide additional insight(s) into relative pressure differences between specific locations or times from which such samples were collected.

In facets method(s) of quantitative analysis of one or more extracted rock volatile(s) associated with PAM development comprise cryogenic trapping and release mass spectrometry analysis. In aspects, cryogenic trapping and release mass spectrometry analysis is associated with RVS methodology (ies) employed as an element of method(s) of PAM development. In certain aspects, method(s) of PAM development comprise the trapping and releasing of, e.g., sulfur monoxide (as described elsewhere herein).

In aspects, the degree of difference between the amount(s) of measured compound(s) in a data set (e.g., PQRVQDs) can impact or help to determine the suitability of attempting to determine GUFP/pressure using the data set. For example, in one aspect, an about 1× to about 5× difference, such as, e.g., a ˜1×-˜4×, ˜1×-˜3×, or a ˜1×-˜2×, difference, such as, e.g., a ˜2×-˜5×, ˜3×-˜5×, or a ˜4×-˜5× difference, as in, for example, a ˜2×-˜4× difference in PQRV quantities in rock material samples collected from different specific locations may provide less reliable insight(s) into significant pressure differences between the at least two different specific locations than, for example, a difference greater than about 5×. In aspects, methods comprise a step of analyzing if the difference in amounts of analyzed rock volatiles, e.g., PQRVQDs, exhibit such a difference and factoring such findings into whether the method will be able to (or is likely to) reliably quantify GU fluid pressure (GUFP).

Factors Impacting Pressure Measurement or Model Use

As indicated elsewhere, other measures of features, components, etc., of geologic unit(s) also can be incorporated/factored into the pressure measurement/quantification methods of the technology and into related objects, such as QAMs/models. This subsection of the disclosure focuses on these potentially important factors of the methods of the technology.

In aspects, the method may comprise accounting for/correcting for conditions/factors present in specific location(s) (aka/ac SL(s) or GUSL(s)) or the overall geologic unit (GU). Such factors can include, e.g., the depth of the specific location, the composition of drilling muds as may be present, and other factors as one of ordinary skill in the art will understand. Correction/accounting can mean adjusting data (e.g., by application of scaling factor(s)) or by, e.g., removal of select data from data set(s) (e.g., removal of PQRVQD based on the presence of associated conditions (ACs) or pressure measurement adjusting factor(s) (PMAF(s)).

A pressure measurement adjusting factor (PMAF) can be any factor that impacts the measurement of PQRV pressure. A PMAF might impact pressure, directly, or may, instead, impact/change the measurement of one or more PQRVs used in or intended for use in the applicable method (e.g., one or more types of carbon dioxide-such as easily extractable rock volatile carbon dioxide (EERVCD)). A PMAF may be specific to the conditions of the GU or a portion of the GU/GU(s) analyzed.

In certain aspects, a pressure measurement adjusting factor (PMAF) can be any PMAF disclosed herein, such as, e.g., total dissolved solids, ionic strength, salinity, temperature, or a combination of any or all thereof. The developer has identified a number of PMAFs and expects that others of skill in the art may identify additional PMAFs based on the principles provided here, with application of routine (not undo) experimentation.

Already identified PMAFs include total dissolved solids (TDS), ionic strength, salinity, pH, temperature, or a combination of any or all thereof (which can impact PQRV concentration and, accordingly, PQRV quantitative data, and, accordingly, PQRVQD derived pressure measurements). As noted previously, oftentimes in the art, TDS and salinity are broadly interpreted as being synonymous.

Qualitative PAMF data may indicate that an existing model is not suitable for use with PQRVQD generated from SLs/GUs that are associated with markedly different PAMF(s) than the PAMF(s) that existed or are known to be acceptable for the existing model. A similar concept is “associated conditions.” Associated conditions do not necessarily have to have a known impact on PQRV pressure measurement. Such conditions may include differences in the characteristics of materials analyzed, locations analyzed, etc., which are suggestive to the practitioner, system, etc., implementing the method that PQRVQD to be analyzed by a method is not suitable for existing model(s), or has a significant, sizable, major, predominate, or general likelihood/risk of not being suitable for use with an existing model. Associated conditions may relate to different lithography, structures, GU compositions, etc. Aspects provide methods that comprise a step of analyzing associated condition data (ACD) to determine/evaluate/assess the suitability of an existing model to a PQRVQD data set or PQRVQD/PMAFQD data set. In this respect, PAMFD, which is qualitative, like ACD (i.e., with or in place of ACD), can be collected and evaluated in methods to determine if a model is suitable for use based on conditions of the geologic materials, geologic unit, or both.

Notably, as described elsewhere, ACD may be utilized in multiple ways, in, e.g., different scenarios. First, ACD may be contemplated in, e.g., the generation of QAM(s), where it may identify certain PQRVQD (with or without PMAFQD) are associated with samples that are not appropriate for inclusion in a QAM, and thus such PQRVQD (with or without PMAFQD) may be eliminated from QAM(s) to establish EQAM(s). Second, ACD may be contemplated in, e.g., the consideration of whether or not PQRVQD (with or without PMAFQD) can be used to measure pressure using a particular QAM; e.g., whether or not a QAM is a good fit for use in determining pressure given some or all of the PQRVQD (with or without PMAFQD). In certain instances, ACD may be used to establish whether a single or multiple QAMs are appropriate for use in determining pressure from PQRVQD (with or without PMAFQD). Likewise, PMAFD, e.g., PMAFQD, may, in aspects, be used in the establishment of QAM(s) and also or alternatively can be used to determine whether or not certain geological sample data is suitable for use in conjunction with certain QAM(s)/EQAM(s) to measure pressure.

A pressure measurement associated factor indicator (PMAFI) is a composition, structural event, state/condition (e.g., pH, temperature, or salinity), or characteristic thereof that indicates the presence of a PMAF (and in aspects the magnitude/extent of a PMAF). For example, sulfate or a proxy thereof (e.g., sulfate/SO) is PMAFI for the salinity of a GU (a PMAF). As noted elsewhere herein, “SO” is often utilized to represent a sulfate proxy, e.g., sulfur monoxide or ion thereof or a fragment (SO fragment or daughter fragment, e.g., daughter fragment ion of sulfate) which, e.g., may be formed by way of analysis by mass spectrophotometry (e.g., being produced at the point at which sulfate may be released from a rock material sample during analysis), where such a proxy can be used as an indicator of sulfate and which may be cleaner to measure (may be more clearly measured) via technology (ies) such as mass spectrometry. SO may, e.g., be described in aspects as a “proxy of a proxy” or an indicator of the PMAFI sulfate (e.g., used as a pressure measurement affecting factor indicator, PMAFII), the PMAFI sulfate being used as a PMAF of salinity. Herein, the use of “sulfate/SO” is commonly used to reference this PMAFI/PMAFII. Other exemplary proxies of salinity may be, e.g., proxies that directly, positively, or linearly correlate with salinity, such as, e.g., chloride (Cl⁻) and, e.g., calcium (Ca²⁺). In aspects, salinity proxy(ies) can be measured by RVS method(s) described herein and can be particularly beneficial for adjusting model(s) described herein. In certain aspects, any identifiable proxy for salinity may be useful for developing EQAM(s) herein, as, e.g., they are indicative of CO₂ dissolution.

Salinity in a GU is a PMAF because high salinity can, e.g., reduce the solubility of a PQRV, such as carbon dioxide, and, accordingly, in relatively high salinity environments, result in a lower carbon dioxide measurement than in relatively lower salinity environments. PMAFs can also reflect elements/factors that directly impact the amount of PQRV in a zone or in SL(s)/material(s), and suitable PMAFIs might also indicate the existence of such state(s)/condition(s). E.g., organic acids, such as formic acid, can, in aspects, be a PMAFI for biogenic activity (a PMAF). In aspects, a PMAFI can be any PMAFI disclosed herein, such as, e.g., mass spectrometry quantified rock volatile organic acid(s), such as, e.g., rock volatile water; rock volatile sulfur monoxide; or both.

In aspects, a pressure measurement adjusting factor indicator (PMAFI) can be any PMAFI disclosed herein, such as, e.g., mass spectrometry quantified rock volatile water, capacitance manometry quantified rock volatile water, mass spectrometry quantified rock volatile sulfur monoxide, or both.

PMAF data or PMAFI data may be qualitative data (reflecting the presence of a PMAF, but without a quantification component) or quantitative data. PMAF quantitative data (“PMAFQD”) means quantitative data associated with a PMAF or a PMAFI. In aspects, PMAFQD is data that is from or derived from a quantitative measure of an element. For example, for rock volatile water quantity can be a directly measured PMFAI (rock volatile water is a PMAFI for water, a PMAF, due to the fact that PQRV concentration typically can be indicative/more indicative of GU fluid pressure than raw/unmodified PQRVQD). In aspects, for example, rock volatile water can be measured by, e.g., capacitance manometry. In other aspects, a quantitative value must be assigned to account for a PMAF. In aspects, measured data associated with a PMAF (e.g., temperature or pH) have to be scaled or otherwise modified to provide modified values that can be best used in an applicable model. In aspects, PMAFQD can be characterized as a quantitative measurement associated with a PMAF (e.g., with a PMAFI) that is suitable for inclusion in a model, and typically leads to a detectable, sizable, or major change in the reliability of a model.

The relationship between PMAFQD and pressure need not necessarily be a positive relationship. In aspects, one, some, most, generally all, or all of the types of PMAFQD in a model exhibit an inverse or negative relationship to GU pressure. In aspects, one, some, most, generally all, or all of the types of PMAFQD in a model exhibit a positive relationship to GU pressure. In aspects, the relationship of PMAF/PMAFI quantity is weakly associated with GU pressure. In aspects, the relationship of PMAF/PMAFI quantity is strongly associated/correlated with GU pressure. In aspects, the determination of weakness or strength is made by relative comparison to the impact of PQRVQD on the measure of GUFP. In aspects, the determination of weakness/strength is made by standard evaluation criteria known in the art.

In aspects, the pressure quantifying data of the method is PQRVQD-derived data that is generated by a relationship between PQRVQD and PMAFQD. In aspects, the relationship is a ratio. In aspects, the ratio is a 1:1 ratio. In aspects, the PMAFI from which the PMAFQD for the ratio is derived/generated is rock volatile water. In aspects, the PAMFI from which the PMAFQD is derived/generated is one or more types of differentially extracted/extractable water, such as easily extracted water (EEW), release resistant water (RRW), or sum/total water (combining EEW and RRW). In aspects, the rock volatile water used as a PMAFI of a method is mostly, generally only, substantially only, or only easily extracted water (EEW). In aspects, a method comprises (1) evaluating if incorporating data reflecting the concentration of water in the one or more specific locations may sizably or significantly positively impact the reliability of the rock volatile carbon dioxide quantity in determining pressure in the geologic unit and, (2) if so, incorporating rock volatile water quantity data into the model (e.g., by generating a PQRVQD derivative comprising a ratio of the PQRVQD to the quantity of rock volatile water for some, most, generally all, or all of the specific locations). In aspects, if reliability of a model may be impacted by the association of water with the analyzed portion of a geologic unit and PQRVQD, method(s) of determining pressure can comprise establishment or use of a ratio of PQRVQD (e.g., rock volatile carbon dioxide (CO₂) quantity) to PMAFQD (e.g., rock volatile water quantity) to aid in establishing a more accurate and reliable evaluation, e.g., measurement, of geologic unit pressure.

In aspects, two, possibly three, or more types of PMAFQD are incorporated into a model to determine GU pressure. In aspects, at least one of the PMAFQD type(s) of input into the model is not presented in relation to PQRVD (i.e., is not in a ratio or other relationship when input/inputted into the model).

In certain aspects, certain organic acids act as PMAFIs in methods (and in more particular aspects, such indicators can be in the form of PMAFI rock volatiles (PMAFIRVs)) (e.g., rock volatile formic acid, as noted above). In other aspects, organic acids (e.g., rock formic acid, acetic acid, or both) are excluded from being used as PQRVs in methods, even if such compounds were identified as carbon dioxide related compounds (“CDRCs”) that may be suitable for supplementing or substituting carbon dioxide in the methods described in certain RVS patents cited in the Background of this disclosure. This last aspect reflects just one surprising/unexpected way in which the technology provided here differs from the technology of the RVS patents, and one of the breakthroughs of the technology in understanding where such RVs may be used in a model and excluded from a model.

In cases, a PMAF may be associated with several related PMAFIs, which may be described as “levels” or some of which may be described as precursors/parents or derivatives/child compositions. For example, sulfate can be a PMAFI with respect to the salinity PMAF or total dissolved solids (TDS) PMAF of a GU/GR. Because sulfate may be relatively more difficult to accurately measure by some analytical tools, e.g., cryogenic trap-and-release mass spectrometry, an indicator of the sulfate PMAFI can also or alternatively be used in the method (e.g., SO/sulfur monoxide). In aspects, different PMAFIs are used that are related to each other by one of the PMAFI(s) being a breakdown product of the parent PMAFI (e.g., as may be the case of sulfate and SO, depending on analytical methods used)

A PMAF and PMAFI may also be composed of the same or similar compounds. For example, water in a GU can be a PMAF (or associated condition, AC), and some or all types of RV water (e.g., EEW) can be used as a PMAFI for the water AC/PMAF.

Suitable steps for evaluating the impact of inclusion of PMAFQD in a model in combination/relationship with PQRVD are known. Such steps can involve, e.g., determination of the reliability of a model, e.g., by performing a regression analysis or other type of reliability/correlation analysis, and comparing the reliability measurements.

In aspects, PMAFI(s) comprise, mostly comprise, generally consist of, or consist of rock volatiles. In aspects, the rock volatiles are measured in situ. In aspects, the rock volatiles are measured after extraction (and optionally isolation or some or all of isolation, concentration, and separation of the rock volatile compounds(s)). In these respects, the methods described herein for extracting, isolating, concentrating, and measuring rock volatile compounds can be applied to such PMAFI(s). Other types of PMAF(s) may be measured through other suitable and known analytical technologies, e.g., gas chromatographic (GC) analysis.

In aspects, where such one or more PMAFs, PMAFIs, or both PMAFs and PMAFIs are deemed to provide detectable or significant impact on the accuracy of geologic unit pressure measurement, method(s) determining pressure can comprise using a ratio comprising one rock volatile to other constituent(s) which detectably or significantly increases the accuracy of geologic unit pressure evaluation (e.g., measurement). For example, method(s) of pressure determination can comprise the establishment or use of a ratio of e.g., rock volatile carbon dioxide (CO₂) quantity to other constituent(s), e.g., a ratio comprising rock volatile CO₂ quantity, rock volatile water quantity, and rock volatile sulfur monoxide quantity improve the accuracy of measurement(s) of GU pressure.

In certain aspects, a PMAF can be any PMAF disclosed herein, such as, e.g., total dissolved solids, ionic strength, salinity, temperature, or a combination of any or all thereof. In aspects, a PMAFI can be any PMAFI disclosed herein, such as, e.g., mass spectrometry-quantified organic acid, such as, e.g., formic acid; water (e.g., as quantified by mass spectrometry or, e.g., capacitance manometry, etc.); sulfur monoxide; or, e.g., combination(s) thereof.

In this and other facets of the technology (ies), the technology (ies) also can be characterized as comprising a “means for” or “step for” providing a recited function, implied function, or known function or, e.g., a “means for” or “step for” performing, participating in, or achieving particular step(s) of method(s). Examples of “steps for” methods provided in this section include “steps for” measuring PMAFQD. Other “steps for” functions described in this section (and elsewhere) include steps for extracting or isolating PMAFs/PMAFIs. Still other “steps for” methods described here are steps for evaluating whether inclusion of PMAFQD in a model sizably or significantly enhances the applicable model. In such a respect, any known or identified equivalents of such named elements can also be, e.g., are, incorporated into method(s) of the technology (ies). As with other sections similarly described herein, any of the facets of the technology (ies) can be, where suitable, described as a means for or step for carrying out a function provided herein (e.g., the above-described means of adjusting pressure prediction of a QAM(s) can be described as pressure prediction adjustment/improvement means or means for adjusting/improving pressure prediction). As noted, the function of a step or means carried out can be explicitly stated, clear by context, or known.

As noted above, some of the Figures provided herein include exemplified procedures, which can be used to determine if the inclusion of one or more types of PMAFQD can improve the measurability of GU fluid pressure based on pressure quantitative rock volatile quantity data (PQRVQD). Uncontradicted, and as suitable, methods or even steps exemplified by such Figures can be employed as aspects or combined with any other aspect of the invention (in this and any other respect) to form other embodiments.

Quantitative Analytical Models and Other Models (Evaluation, Application, and Generation)

A model used to relate PQRVQD to geological unit fluid pressure (GUFP) can be any suitable model. For convenience, three types of models are frequently discussed herein: QAMs, EQAMs, and PAMs/PQAMs. A model typically is or can be expressed as a mathematical relationship or equation(s) or algorithms. In aspects, some facets/parts of a model or an entire model can be graphically represented. In other aspects, a model is more complex than a graphical representation will allow, such as when the number of factors used as inputs is more than two (e.g., where a model comprises input of one type of PQRVQD and two or more types of PMAFQD).

A “quantitative analytical model” (“QAM”) is a model that is known (has been demonstrated/validated) to provide at least reliable GUFP quantitative measurements in at least some contexts. The development of a QAM is exemplified in some of the sections of this disclosure entitled “EXAMPLES.” As with other models, a QAM can comprise any number of suitable relationship(s) between/of the quantity of one or more PQRV(s) and GUFP quantitative measurements (GUFPQMs or sometimes simply “pressure”), optionally where the model further includes the input of quantitative measurements for one, two, or more PMAFI(s)/PMAF(s) (i.e., one or more types of PMAFQD).

As noted, a QAM or other type of model of methods typically can be or can comprise any suitable mathematical model (e.g., a regression model (e.g., linear (e.g., least square), polynomial, or logistic regression), graphical/special model (e.g., a clustering model, such as a K-means clustering model), decision/classification model (e.g., a decision tree model, support vector machine (SVM), k-nearest neighbor (KNN), or Naïve Bayes model, neural network, etc.), or ensemble model (e.g., random forest model), etc.

In aspects, a model of methods comprises or is a regression model. In aspects, a model is a regression model comprising a linear regression. An example of such a regression model is the LINEST/linest function (known/available in Microsoft Excel), which permits users to fit a multiple linear regression model to a dataset using the least squares method and provides an R-squared (R², R2, or R{circumflex over ( )}2-coefficient of determination) calculation or output.

The R-squared/R2 value in such a regression model is a measure of the goodness of fit of the model (which, in applicable aspects, is also referred to here as a measure/indicator of “reliability” of a model). R-squared is a known statistical measure commonly used in regression models that determines the proportion of variance in the dependent variable that can be explained by the independent variable. In other words, R-squared shows how well the data fit the regression model (again, i.e., the goodness of fit). The term “reliability” is used here in this context in this sense, i.e., indicating how reliably the model provides GUFP measures consistent with the fit.

R-squared/R2 quantifies the proportion of the total variance in the dependent variable that is explained by the independent variables in the model. In simple terms, R-squared indicates how well the independent variables predict the variation in the dependent variable. R-squared provides insight into the strength and reliability of a regression model. Stated a different way, the reliability of a measurement, i.a., determines the correlation as measured by R-squared. Thus, readers will understand that the term “reliability” when used in such contexts is not intended to vary any understanding of how such terms may be used in the art and that such usage could be replaced with terms such as “fit” or “strength” of the model. R2 has also been used sometimes as an indicator of statistical “validity” (in other aspects, the term validity here is meant to indicate more generally that the model has been evaluated against otherwise validated data). In this regard, an R2 of test data applied against “gold standard” data, e.g., direct pressure measurement data for a GU or similar context, can be used to validate the model generated for a set of PQRVQD data set or a PQRVQD/PMAFQD data set. Such comparisons are exemplified in the “EXAMPLES” section of this disclosure. Methods for generating models can comprise determining if there is such a close correlation (a reliable correlation or highly reliable correlation) with direct pressure measured data.

R-squared values range from 0 to 1 and are commonly stated as percentages from 0% to 100%. A high R-squared value suggests that the independent variables in the model are effective in explaining the variability in the dependent variable. Conversely, a low R-squared value indicates that the independent variables have little explanatory power, and the model may not adequately fit the data. Stated another way, R2 tells us the percentage of variance in the outcome that is explained by the predictor variables (i.e., the information we do know). A perfect R2 of 1.00 means that our predictor variables explain 100% of the variance in the outcome we are trying to predict. A low R-squared frequently means that a model does not include all [random or unknown] variables that are associated with the outcome.

A model with an R-squared value of 0.9 means that approximately 90% of the variance in the dependent variable is explained by the independent variables. This suggests a strong relationship between the variables and indicates that the model provides a good fit for the data. While there is no universal threshold for what qualifies as a “good” R-squared value, values above 0.7 or 0.8 are often considered strong.

R2 is not the only measure of fit/reliability that can be used. Skilled practitioners will recognize that other indicators/measures may be used with different types of models. Additionally, R2 need not be the only determinant of reliability or fit used in a method to assess a model or the fit of a data set to a model. For example, in contexts, practitioners use error metrics such as root mean squared error (RMSE) and mean absolute error (MAE) to supplement R2 measures. These metrics measure the average distance between the actual and predicted values and can help one compare models or evaluate how well a model performs on new data.

In aspects, a model is considered reliable if it provides an at least about 70% fit/strength/correlation or reliability (e.g., an R2 value of at least 0.7 in a linear regression model, such as what may be output from using the linest/LINEST function in EXCEL). In aspects, methods are performed with a model that has been

In aspects, the determination of pressure is performed with input data (PQRVQD/PMAFQD) that is identified as being a good match, fit, approximately similar in one or more factors, substantially similar in one or more factors (which is included based on prior model use—e.g., as exemplified in the EXAMPLES), or other factors

In certain aspects, method(s) herein comprise development of a QAM that is validated to, or designed/predicted to, at least in part, accurately/reliably determine quantity of at least one pressure quantitative rock volatile, such as, e.g., carbon dioxide (CO₂), measured in a rock material sample such as, e.g., drill cutting sample, collected from a geologic unit. A QAM is characterized by the validation of reliability/accuracy in at least some contexts/settings/situations.

As used herein, an accurate quantitative relationship or set of relationships used in a model employs one or more inputs (here comprising PQRVQD) to determine one or more outputs (here GU fluid pressure—GUFP). In typical aspects, model inputs comprise variables that are expressed in quantitative units, e.g., mass, volume, concentration, time, counts, etc. In aspects, the inputs comprise quantities of two or more types of volatile compounds (e.g., PQRVQD and PMAFQD(s)). In aspects, the one or more outputs are also or alternatively expressed in quantitative units, here in any suitable unit of geologic pressure.

In aspects, the quantitative relationship between/among such inputs comprises a or can be characterized as a mathematical model. In aspects, the mathematical model can be linear or non-linear. In aspects, the mathematical model can be developed using algorithms, such as the linest function in Excel. In aspects, the quality of the predictive relationship can be evaluated based on statistical measures such as a correlation coefficient (R2 or R2) and or p values, such as p<0.05 or p<0.01.

In aspects, a model is reliable (accurate in at least 75% of cases) in terms of GU liquid pressure measurements determined by the model as applied to the PQRVQD and other inputs/relationships of the model (e.g., one, two, or more types of PAMFQD incorporated in the model). In aspects, a model is highly reliable (e.g., at least 85%, >90%, or >95% reliable).

In aspects, a quantitative relationship/model can be shown as a graph (e.g., with x and y coordinates reflecting input variables). In aspects, a quantitative relationship is expressed with or after the application of scaling factor(s) for inputs (applied to input(s)).

In aspects, PQRVQD-derivative data is generated by generating a relationship between PQRVQD and a PMAF quantitative data (PMAFQD), and such PQRVQD-derivative data is input/fed to the model, e.g., in place of raw/unmodified PQRVQD. PMAFQD can be quantitative data regarding a present pressure measurement adjusting factor (PMAF—e.g., salinity, presence of water, presence of structural feature(s) such as a fault, biogenic activity, temperature, pH, etc.) or an indicator of a PMAF (e.g., an RV that is indicative of the presence of a PMAF). In cases, the PMAFQD is a direct measurement that can be directly input into a model (e.g., a quantity of an RV that serves as a PMAF indicator (PMAFI)). In cases, the model assigns a value to a PMAF that can be input into the model to achieve at least reliable pressure measurements in the given context. For example, a value may be assigned to the existence of a structural feature, temperature, or pH of a specific location, and that value is input into a model.

In aspects, the rock volatile water used as a PMAFI of a method is mostly, generally only, substantially only, or only easily extracted water (EEW). In aspects, a method comprises (1) evaluating if incorporating data reflecting the concentration of water in the one or more specific locations may sizably or significantly positively impact the reliability of the rock volatile carbon dioxide quantity in determining pressure in the geologic unit and, (2) if so, incorporating rock volatile water quantity data into the model (e.g., by generating a PQRVQD derivative comprising a ratio of the PQRVQD to the quantity of rock volatile water for some, most, generally all, or all of the specific locations).

In certain aspects, method(s) of determining the pressure within at least a portion of a geologic unit, e.g., method(s) of determining pressure at one or more specific locations, e.g., one or more specific location(s) within at least a portion of a geologic unit comprise a step for evaluating if the inclusion of data associated with at least one pressure measurement adjusting factor (PMAF), e.g., one or more PMAFs disclosed herein, would detectably or significantly improve the accuracy of the pressure measurement. In certain aspects, method(s) of determining the pressure within at least a portion of a geologic unit, e.g., method(s) of determining pressure at one or more specific locations, e.g., one or more specific location(s) within at least a portion of a geologic unit comprise a step for evaluating if the inclusion of data associated with at least one pressure measurement adjusting factor indicator (PMAFI), e.g., one or more PMAFIs disclosed herein, would detectably or significantly improve the accuracy of the pressure measurement. According to aspects, if the practitioner determines (e.g., using principles provided here) or a related system, standard, etc., determines, that inclusion of quantitative data associated with at least one PMAF, at least one PMAFI, or both at least one PMAF and at least one PMAFI (PMAFQD) would detectably, sizably, or significantly improve on the accuracy of a PQRVQD pressure measurement, a method can comprise the step if factoring the relevant PMAFQD into the model and then making the pressure analysis with the adjusted/enhanced model. Similarly (e.g., also or alternatively), if a practitioner determines (e.g., using principles provided here) or a related system, standard, etc., determines, that at least one PMAF, at least one PMAFI, or both at least one PMAF and at least one PMAFI (e.g., as represented by PMAFQD) cause one or geological material samples associated with such PMAF/PMAFI/PMAFQD to not be suitable for use with a particular QAM/EQAM, such geological material samples may be removed from a data set used with a QAM to determine GUSL pressure.

Any of the steps for carrying out methods in this section that relate to subject matter known in the field can be described as a “step for” carrying out a function (or “means for” carrying out a function), as discussed elsewhere. E.g., methods can comprise a step for performing a regression between PQRVQD and PMAFQD(s). Methods also can comprise a step for determining the reliability of a model as applied to input data sets (e.g., using the R2 value provided by a linest function or similar regression model).

As also exemplified in the “EXAMPLES” section of this disclosure, a model (e.g., a PAM/QAM/EQAM) can be applied to a single type or set/collection of rock volatiles in a given context or range of contexts, but also can, in other aspects, be used to evaluate measurements from different volatiles or data sets based on different volatile data. This aspect can be applied in the generation of models, testing the breadth of a model (which is an aspect of model generating facets of the technology), and for the enhancement of models, which are topics of focus of the next two subsections of this disclosure, and also exemplified/discussed elsewhere.

Generation, Use, and Evaluation of Models

In aspects, quantification of geologic unit (GU) pressure is performed by analyzing PQRVQD, either directly or in association with one or more types of pressure measurement adjusting factor quantitative data (PMFAQD(s)), in accordance with a suitable model, e.g., a model that is reliable or highly reliable, at least in some contexts.

In addition to methods of using PQRVQD to determine GU fluid pressure in accordance with a model, this disclosure also provides method(s) of establishing quantitative analytical model(s) (QAM(s)) for relating PQRVQD to GU fluid pressure.

As exemplified/discussed elsewhere, mathematical modeling of a QAM or other type of model is typically based on/comprises a mathematical relationship, such as, e.g., a linear model, e.g., typically a linear regression model, such as, e.g., a least squares regression model. For example, exemplified herein is the use of the LINEST function of Microsoft EXCEL as a QAM for certain types of rock volatile quantity data.

QAM(s) are models demonstrated/validated to be reliable or highly reliable in at least certain contexts (associated with known/characterized rock materials, GUs, PQRVs, ACs, or PMAFs). Model validation can be determined through any suitable method. In aspects, the model itself provides a reliability indicator (e.g., the R2 value provided when performing a linest function in EXCEL).

Where a QAM does not exist, methods comprise the generation of putative analytical model(s) (PAM(s)), testing of the PAM(s), and modification of PAM inputs or relationships to arrive at a QAM that works in the desired, applicable context(s). A putative analytical model is a model that has not been demonstrated to provide or validated with respect to providing at least reliable GU pressure measurements from model inputs (e.g., PQRVQD or PQRVQD-derived data). Such methods may be described as “model development” processes or simply “model development.”

In aspects, a step in developing the quantitative model comprises determining if there is a correlation between the quantity of one or more pressure quantitative rock volatiles (alone or in a relationship with one or more initial pressure measurement adjusting factor quantitative data measurements) and one or more putative items of potential pressure measurement adjusting factor quantitative data and if there is a correlation testing a putative analytical model that incorporates the one or more putative items of potential pressure measurement adjusting factor quantitative data, to determine if the putative analytical model is a quantitative analytical.

In this regard, as discussed above, a measure, such as an R2 measure obtained by performing a linear regression of test data (here PQRVQD or PQRVQD/PMAFQD data) applied against “gold standard” data, e.g., direct pressure measurement data for a GU or similar context, is used to validate a model proposed for a PQRVQD data set or PQRVQD/PMAFQD data set. Such comparisons are exemplified in the “EXAMPLES” section of this disclosure. Methods for generating models can comprise determining if there is such a close correlation (a reliable correlation or highly reliable correlation) with direct pressure measured data.

In aspects, model development can comprise a step for evaluating whether removing one or more data points of pressure quantifying rock volatile data that are associated with one or more rock material samples or specific locations from the geologic unit would increase the accuracy of the model. If removal of one or more data point(s) of pressure quantifying rock volatile data associated with one or more rock material samples or specific locations from the geologic unity would increase the accuracy of the model, in aspects method(s) of model development can comprise re-establishing a PAM after excluding such data from initial data sets (e.g., from initial PQRVQD data). In certain aspects, a basis for removing one or more data points associated with one or more specific locations or one or more rock material samples from a model can be, e.g., a difference in salinity in the different rock volatile measurements initially obtained between the different specific locations or rock material samples originally included in model development.

In aspects, where such factor(s) are deemed to provide detectable, sizable, or significant impact on the accuracy of PAM(s), method(s) of PAM development can comprise using a ratio comprising one rock volatile to other constituent(s) to establish PAM(s) providing reliability in their ability to evaluate and/or predict geologic unit pressure. For example, method(s) of PAM development can comprise the establishment or use of a ratio of, e.g., rock volatile carbon dioxide (CO₂) quantity to other constituent(s), e.g., a ratio comprising rock volatile CO₂ quantity and rock volatile water quantity. In aspects, additional inputs, such as further PMAFQD, e.g., rock volatile sulfur monoxide quantity, are input/factored into a model in establishing a model or developing a QAM.

In aspects, method(s) of model development can comprise a step for evaluating whether removing one or more data points of pressure quantifying rock volatile data that are associated with one or more rock material samples or specific locations from the geologic unit would increase the accuracy of the model. If the removal of one or more data point(s) of pressure quantifying rock volatile data associated with one or more rock material samples or specific locations from the geologic unity would increase the accuracy of the model, in aspects method(s) of PAM development can comprise re-establishing the PAM after excluding such data. In certain aspects, the basis for removing one or more data points associated with one or more specific locations or one or more rock material samples from a model can be, e.g., a difference in salinity in the different rock volatile measurements initially obtained between the different specific locations or rock material samples originally included in model development.

According to certain aspects, method(s) of developing PAMs comprise a step for evaluating whether or not water associated with the analyzed portion of a geologic unit may detectably or significantly negatively impact the reliability of the established rock volatile(s) quantity(ies), e.g., rock volatile carbon dioxide quantity, to be the only rock volatile quantity(ies) relied on in determining pressure in the geologic unit. In aspects, if such reliability may be impacted by the association of water with the analyzed portion of a geologic unit, method(s) of developing PAMs can comprise the establishment or use of a ratio of rock volatile carbon dioxide quantity to rock volatile water quantity to aid in the establishment of a PAM sufficiently reliable to evaluate geologic unit pressure.

According to certain aspects, method(s) of developing PAM(s) comprise determining the geologic unit pressure of one or more specific locations within a geologic unit, which, as described above, can be attained by any applicable known technique or technology (rather than the PQRVQD pressure quantification methods that are newly disclosed here). In certain aspects, such determination is made by direct stem test (DST) or similar technique(s), or, e.g., other technique(s), method(s), or technology (ies) described elsewhere or known in the art.

In certain aspects, method(s) of developing models comprise determining the quantity of one or more pressure quantitative rock volatile(s), e.g., CO₂ or other pressure quantitative rock volatile(s) described herein, associated with rock material sample(s), e.g., drill cuttings, obtained from one or more specific location(s) of a geologic unit for which a pressure determination has been made.

In aspects, method(s) of developing models comprise extracting one or more pressure quantitative rock volatile(s), such as quantitative rock volatile(s) described elsewhere herein, from each of one or more rock material sample(s). For example, method(s) of developing PAM(s) can comprise extracting carbon dioxide from drill cuttings sample(s). In aspects, method(s) comprise subjecting the extracted pressure quantitative rock volatile(s) to one or more quantitative analysis techniques. In certain aspects, the extraction of one or more pressure quantitative rock volatile(s), such as quantitative rock volatile(s) described herein, e.g., carbon dioxide, from rock material sample(s) (such as, e.g., drill cuttings sample(s)) can be accomplished by any suitable technique. In aspects, suitable technique(s) comprise the use of rock volatile stratigraphy (RVS) method(s) previously disclosed and described (at least in brief) herein. In certain aspects, extraction method(s) comprise, e.g., extraction of rock volatile(s) by gentle or weak vacuum, e.g., vacuum pressure(s) of 20 mbar or e.g., other pressure(s) associated with easy extraction as described elsewhere herein, e.g., those associated with RVS method(s).

According to aspects, method(s) of model development comprise a step for determining if an amount of rock volatile(s), e.g., easily extractable rock volatile(s), e.g., carbon dioxide, e.g., easily extractable carbon dioxide, in the samples is able to provide a reliable quantitation of geologic fluid resource pressure. If an/the amount of easily extractable rock volatile(s), e.g., easily extractable carbon dioxide, in the samples is determined as not being able to provide a reliable quantitation of geologic fluid resource pressure, method(s) of PAM development can comprise a step for determining if release resistant rock volatile(s), e.g., release resistant rock volatile carbon dioxide can provide a reliable quantitation of geologic fluid resource pressure.

In aspects, method(s) of model development comprise the use of rock material sample(s), e.g., drill cuttings samples, wherein at least one rock material sample comprises a plurality of rock types, such as, e.g., at least about 2, ˜3, ˜4, or ˜5 or more rock types. In certain aspects, such rock types can be, e.g., sandstone, limestone, or dolomite type rocks. In certain aspects, at least one rock material sample used in PAM method(s) development comprises at least two rock types from among sandstones, limestone, and dolomites.

According to aspects, method(s) of model development comprise a step for evaluating if the inclusion of data associated with at least one pressure measurement adjusting factor (PMAF), such as quantitative data associated with one or more PMAFs/PAMFIs disclosed elsewhere herein, would detectably or significantly improve the accuracy of a model. In certain aspects, method(s) of developing models comprise a step for evaluating if the inclusion of data associated with at least one pressure measurement adjusting factor indicator (PMAFI), such as one or more PMAFIs disclosed herein, would detectably or significantly improve the accuracy of a model. In aspects, if inclusion of data associated with at least one PMAF, at least one PMAFI, or at least one PMAF and at least one PMAFI, is determined to likely detectably or significantly improve accuracy of the PAM, data associated with the at least one PMAF, the at least one PMAFI, or data associated with both, and the PMAFQD is added to the model.

Where a model is being initially developed, directly measured pressure or pressure measured by means independent of PQRVQD (e.g., DST pressure) can be a first input/variable provided to a model. The amount of CO2 is an example of a second variable provided to the model (e.g., PQRVQD). A CO2:water ratio value is another example of a second variable which may be provided to the model (or, e.g., a different normalized value of CO2, e.g., using another rock volatile associated with/reflective of the concentration of the CO2 other than RV water). One or more additional variables, e.g., third, fourth, or fifth (or more) variables, may be provided as described herein, to test whether or not such addition(s) improve the reliability of the model, such as, e.g., the amount of sulfur monoxide. If such addition(s) (or, also or alternatively, subtractions/removal of data as previously stated) increase the reliability of the model, such data is incorporated (or similarly, removed as applicable).

As will be recognized by those skilled in mathematical modeling, a least squares regression model attempts to establish relationship(s) between select input(s) that result(s) a best fit linear relationship between first and subsequent variables. Here, a least squares regression model can be used to evaluate the relationships between such inputs to provide the best linear fit of such data based on variations in the data.

In cases, the pressure quantitative rock volatile data (PQRVD) will provide a more substantive impact on the reliability of a QAM/EQAM than pressure measurement affecting factor quantified data (PMAFQD). In certain alternative cases, PMAFQD will provide a more substantive impact on the reliability of a QAM/EQAM than PQRVQD.

In this and other facets of the technology (ies), the technology (ies) also can be characterized as comprising a “means” for providing a recited function or, e.g., a “means” for performing, participating in, or achieving particular step(s) of method(s), here specifically being means of mathematically modeling the relationship of the quantity of one or more PQRV(s), optionally with PMAFIQD, in a quantitative analytical model (QAM). In such a respect, any known or identified equivalents of such named elements can also be, e.g., are, incorporated into method(s) of the technology (ies). As with other sections similarly described herein, any of the facets of the technology (ies) can be, where suitable, described as means (e.g., the above-described means of mathematically modeling the relationship of the quantity of one or more PQRV(s), optionally with PMAFIQD in QAM, can be described as mathematical modeling means or means for mathematically modeling).

According to aspects, method(s) of directly determining the pressure within at least a portion of a geologic unit, e.g., method(s) of determining pressure at one or more specific locations, e.g., one or more specific location(s) within at least a portion of a geologic unit comprise determining the pressure of geologic fluids at one or more specific locations. In aspects, determination of the pressure of one or more geologic fluids can be attained by any applicable technique or technology. In certain aspects, such determination is made by direct stem test (DST) or similar technique(s), or, e.g., other technique(s), method(s), or technology (ies) described elsewhere herein. Such direct pressure measurements can be input into a model along with PQRV data to establish a putative model that can be evaluated to determine if the putative model can act as a QAM in a reliable manner.

According to aspects, method(s) herein can comprise obtaining one or more rock material sample(s), such as, e.g., drill cuttings sample(s), from one or more specific location(s) of a geologic unit for which a pressure determination has been made, e.g., by one or more direct measure(s) such as, e.g., by DST. In certain aspects, the method(s) comprise determining the quantity of the one or more pressure quantitative rock volatiles associated with each of the rock material sample(s) obtained from the one or more specific location(s) of a geologic unit for which a pressure determination has been made.

In aspects, the pressure measurements may be obtained in situ, for example, by drill stem testing. In aspects, a geologic material may be removed from the specific location for analysis to determine the pressure at the specific location from which the geologic material was removed. Such pressure measurement may be made by any number of techniques as would be understood by one of ordinary skill in the art. In aspects, the quantitative pressure measure may be obtained in situ in about 15 minutes to 1 hour, about 1 hour to about 2 hours, or about 2 hours to about 6 hours or more.

In aspects, the quantitative pressure measurement value of a model in development or as used to develop/validate a model (establish that the model is reliable) is expressed after the pressure at the specific location in a borehole environment reaches equilibrium. In aspects such a direct pressure measurement value is expressed as a discrete value wherein the pressure at the specific location does not equilibrate during the time of measurement, and the discrete value is determined as a function of pressure and time while the pressure measurement was obtained.

In methods, a practitioner may have to determine the applicability of a known model to a data set. As exemplified in the “EXAMPLES” and discussed elsewhere, PQRVQD and PQRVQD-derived models of GU pressure can have broad applicability (across different fields, different locations, or both, etc.). Accordingly, a known model may be suitable for use with input data that is derived from a somewhat different context (e.g., different rock materials, presence of different qualitative pressure measurement adjusting factors, different alternative conditions, etc.). The evaluation of the applicability of a known model to a new test set of data, a different context, or both, can be a step in methods of the technology. The evaluation can comprise consideration of such factors using known principles in the art (e.g., similarity in salinity, similarity in GU features, or similarity in terms of any one or more of pH, temperature, biogenic activity, etc.). The developer expects that such determinations can often be made based on the principles and examples provided herein without the requirement of undue experimentation in making such evaluations. In aspects where there is doubt about the applicability of a data set to a model or the effectiveness of a model with respect to a data set, methods can comprise evaluating the model or generating a new model based on direct pressure measures. In certain facets, a beneficial contribution of technology (ies) herein comprises the use of one or more, such as, e.g., two or more quantitative analytical model(s) applied to a set of data collected from a plurality of geological material (e.g., rock material) samples to obtain a reliable, e.g., a highly reliable, pressure measurement at each SL from which the sample(s) were obtained. In certain aspects, data from one or more geological material (e.g., rock material) samples may be selectively removed from a data set to improve upon the applicability of a known model to the remaining data set, e.g., to improve upon the model's ability to provide reliable/highly reliable pressure measurement(s) at the SL(s) from which each geological material sample represented by the remaining data set was collected.

In this and other facets of the technology (ies), the technology (ies) also can be characterized as comprising a “means for” or “step for” providing a recited function or, e.g., a “means/step for” performing, participating in, or achieving particular step(s) of method(s). For example, the models or the such steps in respect of generation, evaluation, or application of a model can comprise any suitable steps for/means for evaluation of the strength/fit (reliability) of the model as applied to a data set (e.g., applying equivalents to R-squared/linear regression analysis) may be performed by equivalents and, accordingly, described as either “means for” or “steps for” carrying out a function (e.g., step for determining the correlation between data in a data set, step for determining the correlation between test (PQRVQD or PQRVQD/PMAFQD data) and gold standard data (e.g., direct pressure measurement data), etc.). Steps/means for enhancing one or more analytical model(s) in relationship to data sets, as described in this subsection or the next subsection of this disclosure, also can be carried out by known equivalents to the methods described herein (e.g., QAM(s)) can be described as analytical model enhancement means, and further, the above-described means applied to one or more initial analytical model(s) to detectably or significantly enhance such models can be described as enhancement factor means, or means for analytical model enhancement and means for application to analytical model(s) for enhancement of the same.

Enhanced Models/Model Refinement

In addition to methods of using PQRVQD to determine GU fluid pressure in accordance with a model, this disclosure also provides method(s) of establishing quantitative analytical model(s) (QAM(s)) for relating PQRVQD to GU fluid pressure.

In aspects, methods comprise improving/enhancing models (i.e., generating enhanced quantitative analytical models (EQAMs)). As described elsewhere, enhancement of a model can be accomplished by, or comprise, e.g., removing data from the QAM (e.g., removing PQRVQD data point(s) from an initial PQRVQD data set) and thereafter subjecting the modified data set to a suitable mathematical model.

As with QAMs, mathematical modeling of a putative EQAM is typically performed by or comprises a linear model, e.g., typically a linear regression model, such as, e.g., a least squares regression model. For example, exemplified herein is the use of the LINEST function of Microsoft EXCEL to provide a model that can be used to analyze PQRVQD or PQRVQD/PMAFQD data sets.

In an exemplary aspect, methods comprise evaluating if the model indicates that the pressure quantitative rock volatile quantity data provides a high reliability correlation (aka/ac a high fit correlation or correlation with high strength) and (2) (a) removing some of the pressure quantitative rock volatile quantity data from the pressure quantitative rock volatile quantity data set initially included in the model, (b) incorporating pressure measurement adjusting factor data, pressure measurement adjusting factor indicator data, or both (e.g., incorporating PMAFQD) into the model, or (c) both, and (d) repeating (a), (b), or (c) until the enhanced/revised model indicates that the pressure quantitative rock volatile quantity data provides a high reliability measure of pressure in the one or more specific locations.

As exemplified by the Figures and related disclosure provided in the “EXAMPLES” section, the step of modifying types of inputs, modifying data in the tested data set, or both, to arrive at a model with desired properties (e.g., an indication of reliable or highly reliable fit) can involve two, three, four or more rounds of iteration of such changes an evaluation of the resulting output of the model.

As noted, in exemplary aspects, a quantitative relationship of inputs/model can be further improved by adding or subtracting one or more input values (type of data, or data point(s) in a data set of a type of input—e.g., data points in a PQRVQD data set). In aspects, a quantitative relationship/model can be shown as a graph (e.g., with x and y coordinates reflecting input variables). In aspects, a quantitative relationship is expressed with or after the application of scaling factor(s) for inputs (applied to input(s)).

Application of Methods Over Geologic Units or Over Time

Method(s) herein are applicable to the determination of pressure in one or more subsurface geologic site(s). In aspects, a geologic site is a site relevant to oil, gas, or oil and gas exploration. In aspects, a geologic site is a site relevant to carbon sequestration activity(ies), e.g., CCS/CCSU consideration, exploration, or activity(ies). In certain aspects, method(s) herein are applicable to the determination of pressure in one or more subsurface geologic site(s) unrelated to oil, gas, or oil and gas exploration. In aspects, method(s) herein are applicable to the determination of pressure in one or more subsurface geologic site(s) unrelated to carbon sequestration and related exploration or activity(ies). In aspects, method(s) herein are applied to subsurface site(s) for purpose(s) unrelated to oil, gas, or oil and gas exploration and unrelated to carbon sequestration-related activity(ies).

Methods of the presently described technology can advantageously and surprisingly be applied to the determination of pressure across portions of a geologic unit or across geologic units or even across different time periods in one or more geologic units.

As exemplified by the disclosure in the “EXAMPLES” section, methods of the invention can include determining pressure in different portions of a geologic unit, in different geologic units, or both. In aspects, as also exemplified by the EXAMPLES, a model may be capable of determining GU pressure reliability or with high reliability/fit even when the model is applied to PQRVQD/PQRVQD-derived data sets from different parts of a geologic unit (e.g., different formations) or from different geologic units (e.g., different fields). In aspects, methods can comprise obtaining RV quantities directly or obtaining samples from different portions of a geologic unit (e.g., a basin, a play, or a field) or from different geologic units (different fields, different sites, different boreholes, or different zones (e.g., reservoirs or formations)) and comparing pressure measurements for such portions or different geologic units. Such different measures can be used, e.g., to evaluate geologic units, to identify possible new geologic units or zones, etc., for exploration, production, or exploitation of a geologic resource, etc.

As also exemplified by the disclosure in the “EXAMPLES” section, methods of the invention can include (1) determining pressure quantitative rock volatile quantity data (PQRVQD) or PQRVQD-derived/dependent geologic unit (GU) pressure measurements at different time points (e.g., times separated by 5, 10, 15, 20, 25, 35, 50, 60, 70 or more years) by evaluation of rock materials at different times or by the evaluation of rock material samples collected at different times (in a model that is validated as being at least reliable and is applicable for the data set based on correlation with direct pressure measurements in the context or in similar contexts) or (2) making a comparative analysis of PQRVQD or PQRVQD-derived/dependent data (even without such a validation step). Thus, for example, rock material samples used in method(s) may be collected from a geologic unit (e.g., a basin, a play, or a field) or from different geologic units (different fields, different sites, different boreholes, or different zones (e.g., reservoirs or formations)) at different points in time, e.g., points in time separated by minutes, hours, days, weeks, months, years, decades, or even centuries, or both.

In aspects, a data set or model can determine pressure at the present time. In aspects, present pressure information can help to assess risk and, e.g., plan activity(ies), for example, in a field that is being or has been depleted due to past production activity. In certain further aspects, a data set or model based on changes over time can be used to predict future pressure changes in one or more geologic units. E.g., methods can comprise the use of rock material samples collected from a geologic unit at different points in time, e.g., different points in time separated by minutes, hours, days, weeks, months, years, decades, scores of years, or other periods. Such methods can comprise, e.g., determination of a pattern of change in pressure over time associated with another factor, e.g., a PMAF, in aspects, a PMAF associated with PMAFQD. For example, biogenic activity, erosion of structure or migration of geologic resources from the geologic unit, and other known factors that can change the geologic resources in or other contents of a geologic unit, may be associated with pressure changes over time that can be captured, and from such changes one or more future scenarios generated, and from such generated scenarios a prediction of future pressure in the relevant GU(s) made (e.g., a simple predictive relationship might be generated from a linear loss in pressure determined over time using PQRVQD or PQRVQD-derived data that could be used to predict the future pressure state by extending the pressure line by the slope reflecting the known loss of pressure to a future time point in a graphical model).

Guiding Resource Utilization as Directed by PQRVQD or PQRVQD-Derived Pressure Measurements

According to aspects, method(s) of pressure determination described here allow for pressure measurements to be obtained which can be used to direct geologic resource utilization/exploitation, including, e.g., resource extraction or utilization activities.

In aspects, such resource extraction activity(ies) can be ongoing in real-time, or such activity(ies) can be planned activity(ies) related to the geologic unit from which the pressure measurement(s) are obtained.

In aspects, methods are performed in part of a currently producing site/well or in areas nearby or adjacent/connected thereto, such as in one or more new horizontal wells/boreholes placed close to other horizontal wells/boreholes or run off an existing vertical well. Application of methods to such nearly wells/boreholes can also apply to previously active wells/sites, dry (non-producing) wells/sites, or prospective wells/sites. Thus, methods can be practiced in association with, e.g., currently producing and non-producing wells, online wells, wells not yet brought online, and dry (non-producing) wells. Methods can be applied herein to guide activity in or about vertical well(s), horizontal well(s) (wells typically including both horizontal and vertical sections), and vertical/horizontal section(s) of well(s). Methods herein can be used to guide activities such as the placement of new horizontal wells, new vertical wells, etc. Methods herein can be used to direct the application of enhanced oil recovery (EOR) methods, such as fracking activities. For example, PQRVQD/PQRVQD-derived pressure data measurements can be used, i.a., to identify GU characteristics (e.g., tight spaces, faults, or other features or activities, such as biogenic activity) that can aid in the determination of whether EOR activities should be investigated or performed in the relevant zone/site/GU.

In aspects the methods provided herein can, among other things, aid in the evaluation of, or determine, whether additional exploration/drilling of such a dry well, or other prospective site should be considered, which can be indicated by cuttings from such a well sharing characteristics with that of nearby productive wells (or wells meeting similar patterns of data, including the PQRVQD/PQRVQD-derived data measurement of pressure as determined by the application of the method(s) of this technology).

In this and other respects, methods can be used here to direct geologic resource exploration or extraction/production (e.g., pressure data obtained by performing methods of the technology can be used to “guide” or direct explorative or productive drilling or EOR, etc.).

Methods of the technology, as indicated elsewhere, can also be applied to evaluate a GU for other GU-related activities in addition to geologic resource extraction/production. For example, the determination of GU pressure can be useful in carbon sequestration (CCS/CCUS) settings. A method can comprise, e.g., determining if a site is undergoing a loss of pressure over time (indicating attention may be required in respect of a current site or making a prospective site unsuitable, etc.). Relevant methods relating to pressure loss are known and can be adapted for use with pressures determined by the present methods.

Advantages and Unique Characteristics of Embodiments

There are a number of features/advantages attendant to aspects of the technology herein that can be used to further characterize methods of this technology.

As noted, methods of the technology and attendant models can be (and in exemplary aspects have been demonstrated to be) at least reliable, or highly reliable (provide a strong correlation with direct pressure measurements), when applied to geologic materials taken from different formations within a geologic unit, separate sites within a geologic unit, or both. In other words, the developer has shown that PQRVQD data sets and associated known models can be reliably used in different contexts. E.g., the same model can be applied to PQRVQD or other rock volatile data (e.g., PQRVQD and PMAFQD) obtained from different sites, e.g., different wells, in a geologic unit, e.g., a field, a play, or a system (a basin or province), or even across different field GUs in a larger GU (system or play).

A substantial advantage of the methods provided herein is the ability of the practitioner to now obtain a significantly greater degree of resolution of pressure data in a GU than was previously practically possible through direct pressure measurement methods such as DST. A method of the technology can include, e.g., providing measurements from or samples taken from, e.g., at least 10, 20, 30, 40, 50, 100, or more specific locations of a GU, e.g., 5-500, 5-250, 10-200, or 15-300 pressure measurements for a corresponding number or approximately corresponding number of specific locations within the at least portion of the geologic unit based, in at least part, on the quantity of the one or more pressure quantitative rock volatiles. Such determinations made through RVS techniques or other suitable methodology/means/steps can provide a refined pressure map of a GU that would have been deemed to be prohibitively expensive using methods such as DSTs.

Moreover, methods of the invention can be effectively employed in environments that are either very impractical or impossible for conventional direct pressure measurement methods to operate. For example, DST normally will not work in unconventional well environments, tight formation environments, or both (which commonly occur together). DST equipment normally can only be employed in vertical well settings that are not associated with tight formations. In contrast, methods of this technology can readily obtain pressure measurements associated with such environments.

The above-described advantages are exemplary of the many advantages provided by this new technology, any of which can be used to further characterize methods or other embodiments provided by this disclosure.

Computer Applications/Systems

Readers will appreciate that the methods and other embodiments of the technology can be used to provide new computer systems/devices and applications, and that methods of the technology can be implemented in various aspects by computer systems.

For example, in one exemplary aspect the technology provides a computer system comprising (1) a computer processor, (2) memory, (3) an input component, and (4) an output component, wherein the computer program comprises one or more engines that are programmed to apply one or more quantitative analytical models described in the preceding subsections of this Detailed Description or in other portions of this disclosure. In another aspect, the technology provides a computer system programmed to selectively, automatically, or selectively automatically, or a combination thereof, factor one or more quantitative analytical models that factor in pressure measurement adjusting factor data, pressure measurement adjusting factor indicator data, or both, into the quantitative analytical model.

Readers will also appreciate that the technology provides computer systems that comprise an artificial intelligence (“AI”) system/model (“system”) (e.g., a trained neural network), wherein the AI model is trained on data set(s) comprising various types of data described herein—e.g., PQRVQD, PQRVQD and PMAFQD, or PQRVQD or PQRVQD-derived data in combination with direct pressure measurement data associated (which may be, e.g., associated with a type of geologic unit, associated condition data, or other data, e.g., qualitative PMAF data). Methods involving such systems/devices can comprise submitting pressure quantitative rock volatile quantity data to the AI model and permitting the artificial intelligence system to analyze pressure quantitative rock volatile quantity data to automatically or semi-automatically provide pressure quantitation data based on the analysis performed by the artificial intelligence system. Such methods also can comprise, e.g., permitting the AI system to recommend or select a model or to even possibly generate a model for assessing an applicable test data set of PQRVQD or PQRVQD-derived/comprising data (the term “PQRVQ-derived data” herein meaning, uncontradicted, any data set comprising PQRVQD—e.g., a PQRVQD/PMAVQD ratio data set or a data set comprising PQRVQD and PMAFQD separately or PQRVQD-derived data, such as a ratio of PQRVQD/PMAFQD and separate PMAFQD data and providing implicit support for all such types of data sets as separate aspects).

Further, computer system(s) (in aspects with or without AI) of technology (ies) herein may, in aspects, determine which data of a data set is appropriate for use with particular model(s). In aspects, computer system(s) can parse data of a data set and assign relevant portion(s) of a data set to one or more QAM(s) for pressure determination. Computer system(s) (in aspects with or without AI) of technology (ies) herein may in certain aspects assess data of data set(s) and evaluate whether selective removal of certain data improves the reliability of one or more QAM(s) to provide reliable/highly reliable pressure measurements for specific locations associated with remaining data of the data set (that is, to provide reliable/highly reliable pressure measurement for the specific locations from which geological samples were collected and wherein data from such geological samples remain part of the data set.)

Comparative Methods with PQRVD/PMFAD

Besides the pressure quantitation methods and related aspects described above, this technology also provides new comparative estimation quantitation methods, e.g., methods relating to the comparison of relative amounts of PQRVs first identified in this disclosure, even where such comparison is made without reference to a model (or even where such a model does not exist). For example, in one aspect, the technology provides a method of comparing EERVCD measurements from one portion of a GU to another portion of a GU (e.g., one site to another, one zone to another, etc.), between different GUs, or over time in a GU. Similarly, methods can comprise, e.g., comparison of any of the various PMAFs/PMAFIs described herein (e.g., RV sulfate or RV sulfate/SO from one time or location to another time or location). Such methods can, e.g., provide relative indications of zones/areas or times of relatively high pressure or pressure measurement-associated conditions.

Such qualitative method(s) described here, uncontradicted, can be performed alone or in combination with quantitative method(s) described elsewhere herein. For example, a method can include making one or more quantitative measurement(s) in a zone of a site to obtain a first set of PQRVQD, PMAFQD, or both, followed by comparing the first set of quantitative data to the measures of PMAFQD, PQRVQD or both of a second data set when, for example, there may be doubt about whether the measures of the second site (data from the second quantitative data set) can be reliably fit to the data of the first data set.

Means/Steps for Performing Functions of Methods/Embodiments

As discussed herein, aspects of the technology also can be described as “means for” or “steps for” providing features or functions, respectively (the construction of such terms is known and further described in the CONSTRUCTION AND TERMS section of this document) (these terms, “step” and “means” may be used interchangeably herein).

Method(s) provided by the technology can accordingly be characterized as step(s) for performing functions or, e.g., element(s) participating in such step(s) or which, e.g., facilitate the performance of step(s), particularly where equivalents to steps or other elements herein are known in the art.

As noted elsewhere in general, any element described herein as a “means” or for performing a function can also, wherever suitable, serve as a “step for” performing a function in the context of method(s) of the invention, and vice versa in applicable aspects. E.g., an element described herein as a means for pressure quantification also simultaneously and implicitly supports a method of developing a quantitative analytical model comprising, e.g., use of such means and, e.g., a method comprising a step for carrying out the function of the means (determining a quantity of pressure).

One example of such aspects includes performing a step for or providing a means for determining the direct pressure within at least a portion of a geologic unit (comprising, e.g., equivalents to performing DST and other recited direct pressure techniques).

Another example of such aspects includes performing a step for or providing a means for collecting rock samples, with respect to rock sample type(s).

Methods of the technology can comprise, e.g., a step for determining the quantity of one or more pressure quantitative rock volatiles associated with one or more geologic materials of or from one or more specific locations within the geologic unit. E.g., methods can include performing a step for or providing a means for analyzing rock volatile amounts in a material through in situ analyses or through methods applied to rock material samples (RVS, gas chromatography, etc.).

Methods can comprise a step for generating a model, assessing the reliability of a model with respect to a context, or evaluating the fit of a model to a data set. Numerous suitable equivalents and variations of the methods provided herein may be known in this respect.

Methods can comprise a step for the extraction of rock volatiles from rock materials or, where applicable, the isolation or the concentration of rock volatiles (e.g., by gentle vacuum extraction or by application of a gentle vacuum equivalent force to similarly extract the RVs in a suitable manner). A step for trapping at least some of the one or more rock volatiles may comprise equivalents to a cryotrap or other trapping system disclosed herein. A step releasing the trapped/concentrated rock volatiles can comprise, e.g., chromatographic release, temperature release from a cryotrap, or known equivalents.

Methods can include a step for relaying data reflecting the analyzed content of the rock volatiles in the different portions to one or more systems or persons involved in geologic material operations (e.g., by automatic or semi-automatic email, text message, other instant message, triggering warning/event indicators, or similar data relay, etc.).

Methods can include, e.g., a step for performing geologic material operations that are guided at least in part by the relayed data (e.g., guiding exploration, guiding production, or guiding sequestration activities, etc.). Geologic material operations may include drilling, performing EOR, performing sequestration methods, or equivalents.

Methods can comprise a step for determining the reliability of the quantity of the pressure quantitative rock volatiles to act as an independent predictor of geologic unit-specific location pressure.

Methods can comprise a step for obtaining pressure measurement adjusting factor quantitative data from one or more geologic materials (e.g., measuring salinity, pH, TDS, or temperature by various known methods).

Methods can comprise a step for evaluating a model, such as a step for evaluating if incorporating the pressure measurement adjusting factor quantitative data in a model with the quantity of the one or more pressure quantitative rock volatiles results in a reliable model or a more reliable model (e.g., inputting the PMAFQD into a regression model, such as a least squares linear regression model along with the PQRVD or PQRVD-related data (e.g., a PQRVQD/PMAFQD ratio) or equivalents thereof) and, where applicable a step for incorporating the pressure measurement adjusting factor quantitative data into the model (e.g., inputting the additional data set or a derivative thereof to the model—e.g., as an input to LINEST or its equivalent) to generate an enhanced quantitative analytical model and using the enhanced quantitative analytical model.

In one aspect, method(s) of the invention comprise means of indirect measurable pressure quantitation by pressure-quantitative element(s) (“pressure-quantitative element(s) means”). Support for pressure-quantitative element(s) means can be found in, e.g., the section(s) entitled “ROCK VOLATILES,” “PQRVs: PRESSURE QUANTITATIVE ROCK VOLATILES,” or both.

In one aspect, method(s) of the invention comprise means of indicating pressure, e.g., volatile means of indicating pressure (“pressure indicating means” or “pressure quantifiable indicating means”). Support for pressure indicating means can be found in, e.g., the sections entitled “PQRVs: PRESSURE QUANTITATIVE ROCK VOLATILES,” or both.

In one aspect, method(s) of the invention comprise means of quantifying pressure (“pressure quantification means”). Support for pressure quantification means can be found in, e.g., the sections entitled “QUANTITATION OF FLUID PRESSURE THROUGH PQRVQD/PQRVQDDD, “FACTORS IMPACTING PRESSURE MEASUREMENT OR MODEL USE,” or both.

In one aspect, method(s) of the invention comprise means of mathematically modeling the relationship of the quantity of PQRV(s), with or without PMAF(s)/PMAFI(s), in analytical models such as, e.g., PAM(s), QAM(s), or both (“mathematical modeling means.”) Support for mathematical modeling means can be found in, e.g., the section entitled “GENERATION, USE, AND EVALUATION OF MODELS.”

In one aspect, method(s) of the invention comprise means of adjusting pressure prediction (e.g., within a QAM) or, e.g., means of improving reliability of quantitative analytical model(s) (“pressure prediction adjustment means.”) Support for pressure prediction adjustment means can be found in, e.g., the section entitled “FACTORS IMPACTING PRESSURE MEASUREMENT OR MODEL USE.” In one aspect, method(s) of the invention comprise means of enhancing one or more analytical model(s) (“analytical model enhancement means”). In another aspect, method(s) of the invention comprise means applied to one or more initial analytical model(s) to enhance such models (“enhancement factor means.”) Support for analytical model enhancement means and enhancement factor means can be found in, e.g., the section entitled “ENHANCED MODELS/MODEL REFINEMENT.”

In one aspect, method(s) of the invention comprise means of extracting one or more pressure quantitative rock volatile(s), such as, e.g., quantitative rock volatile(s) (“rock volatile extraction means.”) Support for rock volatile extraction means can be found in, e.g., discussion(s) of rock volatile stratigraphy and other such method(s) throughout this disclosure.

REPRESENTATIVE EXPERIMENTS/EMBODIMENTS (“EXAMPLES”)

To further exemplify and illuminate aspects of the disclosure, the following description of illustrative applications of particular aspects or related principles is provided. These “Examples” are meant to exemplify particular facets of the disclosure/technology but should not be used to limit the scope of the described technology/disclosure in any manner.

The exemplary data provided herein demonstrate that a model for pressure, as determined by drill stem testing (such as, e.g., DST and similar technology (ies)), within a well, across formations within a single well, across wells, across formations within multiple wells, across rock characteristics such as, e.g., size, type, etc., and across fields, and still further, across time, can be developed which allow for the measurement of pressure without the need for use of technologies such as DST and others. Such model(s) avoid the time and expense of such technologies and allow for faster and less expensive pressure determination. Model(s) described at least in part by the Examples provided herein incorporate the measurement of the amount of CO₂ in rock material samples, e.g., drill cuttings samples. Further, model(s) such as those described/exemplified here allow for studies that look backward in time, something traditional direct pressure measurement technologies do not allow. That is, traditional pressure measurement technology (ies)/method(s) can typically only be carried out before or during completion of, e.g., a well; one is not able to return in month(s) or years(s) to collect additional data, e.g., especially if one is not producing from the same zone. Model(s) may also incorporate the measurement of the amount of water, e.g., easily releasable water, in rock material samples, such as drill cuttings samples. Model(s) may also incorporate the measurement of the amount of salinity-correlated volatile compound(s), such as, e.g., sulfate/SO in rock material samples, such as, e.g., drill cuttings samples. In certain aspects, model(s) comprise only the measurement of CO₂ in rock material samples, such as, e.g., drill cuttings. In certain aspects, model(s) comprise the measurement of CO₂ and water, e.g., easily releasable water, in rock material samples, such as drill cuttings. In certain aspects, model(s) comprise the measurement of CO₂, water (e.g., easily releasable water), and salinity-correlated rock volatile compound(s) (e.g., sulfate/SO) in rock material samples, such as drill cuttings.

The Examples which follow further demonstrate that rock material samples, such drill cuttings samples, used in model(s) described herein can be samples having been just collected or having been collected in the past; not just in the near past but many, many years in the past, such as, e.g., up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, or more years or more in the past. Such rock material samples need not be, or need not have been, protected from the atmosphere in any specific way from the time of their collection to the time they are analyzed for such analytes. Rock material samples can be, e.g., drill cuttings samples greater than 80 years old—that is, up to or more than 80 years having passed from the time the samples were collected to the time they are analyzed. In aspects, analysis of rock material sample(s) of model(s) provided herein utilizes rock volatile stratigraphy method(s) or equivalent method(s), such as, e.g., method(s) comprising use of a 20-mbar vacuum extraction step.

A number of Examples exemplifying the above select inventive feature(s) of this disclosure follow. Note that drill stem test (DST) pressure data is discussed in several Examples herein. When discussed, the initial shut in pressure (ISIP) is used as the formation pressure as measured by DST.

Example 1

This example demonstrates how field history can be determined, at least in part, based on the quantitative measurement of carbon dioxide (CO₂) (e.g., obtaining volatile carbon dioxide quantitative data (QD) in samples collected from multiple locations across a formation, e.g., from two different borehole environments drilled at different times with different technology (ies).

Patterson 1-23 (“P123,” or “Well 1”) is a well that was drilled in 1941. Drill cuttings were collected from various locations within the borehole as it was being drilled. From the time the drill cuttings were collected, they were not protected from ambient environmental conditions, e.g., atmospheric temperature, pressure, and humidity, in any specific manner, but rather routinely stored without any such specific isolation.

P123 (Well 1) is located in Kearney County, Kansas, near the city of Lakin. P123 is located in the Patterson Site/Field, a field that produces petroleum from the Morrow Sandstone (“Morrow”) formation. At the time P123 was drilled, the pressure in the Morrow formation of P123 was measured and recorded as 1,125 psi. The Meramec formation is also represented within P123. Note that the Patterson Site is larger than the Patterson Field and also contains the Hartland field.

In 2010, Enhanced Oil Recovery (EOR) efforts, represented by water flooding, of the Patterson field were initiated through injection wells to increase the field's petroleum production. Herein, and discussed in more detail elsewhere, water flooding is an example of a pressure measurement affecting factor (PMAF). The Patterson field reached a peak of production of about 230,000 barrels in 2013, at which time the field comprised about 41 wells. As of 2023, the Patterson field comprised about 13 active wells, and the petroleum production had dropped to about 5,000 barrels.

In 2020, Patterson KGS 5-25 (“P525,” or “Well 2”) was drilled as part of the Kansas Geological Survey (“KGS”) Phase II CarbonSAFE program. The program evaluated freshly obtained drill cuttings (e.g., from P525 (Well 2)) as well as legacy drill cuttings from P123, among other wells. P525 is located approximately 2 miles from P123. Like P123, both the Meramec and the Morrow formations are also present in P525.

A plurality of drill cutting samples were obtained from numerous locations within each of P123 and P525.

In both P123 and P525, drill cuttings were collected from locations representing about 10-foot or about 20-foot segments (zones) of the well.

Underlying geological formations containing petroleum reserves are not necessarily located the same distance below the Earth's surface across multiple wells. The Meramec and Morrow formations are represented in each of the wells, P123 and P525; however, each is not present at the same depth in each well. Because of this fact, drill cutting locations of drill cutting samples for each well were identified relative to the depth of the formation as it exists in each well. Such relative positioning is reflected as “Depth Hung” in the Figure(s) provided herewith. Specifically, specific locations where samples were collected located closer to the Earth's surface than the formation were given positive (+) values (e.g., a positive “Depth Hung” value), while locations further from the Earth's surface than the formation were given negative (−) values (e.g., a negative “Depth Hung” value).

The collected drill cuttings were analyzed by the “gentle vacuum” extraction, cryogenic trapping, controlled warming, and mass spectrometry rock volatile stratigraphy (RVS) method(s) described elsewhere in this disclosure (described in detail in the Prior Smith patents). As described elsewhere herein, a gentle vacuum is a vacuum pressure sufficiently strong to obtain, e.g., a suitable amount of volatile(s), e.g., rock volatiles, but which does not detectably, sizably, majorly, or significantly destroy the volatile(s). The collected drill cuttings were more specifically analyzed by RVS method(s) comprising application of sufficient vacuum to characterize volatile(s) released by the method as “easily extractable” volatiles (“easily extractable” being discussed elsewhere herein), such as, e.g., “easily extractable rock volatile carbon dioxide” (EERVCD). Such a vacuum is referenced elsewhere herein as, e.g., a low extraction force gentle vacuum.

RVS method(s) capable of applying still gentle but a bit higher vacuum, e.g., 2 mbar, were also employed to, e.g., facilitate the measurement of certain compound(s) present in the drill cuttings samples. Such volatile(s) released under these condition(s) are characterizable as “release resistant” volatiles (“release resistant” being discussed elsewhere herein). Such a vacuum is referenced elsewhere herein as, e.g., a high extraction force gentle vacuum. (Of note, instrumentation of RVS method(s) is capable of utilizing workflows applying a broad range of vacuum pressure, e.g., including applying condition(s) at near atmospheric pressure to condition(s) orders of magnitude higher than what is described herein as a high extraction force gentle vacuum.)

The amount of water released by application of a gentle vacuum (20 mbar) (“easily extractable (or extracted)” water, EEW) was determined, as was the total amount of water present in each sample, facilitated by the further application of a greater vacuum (2 mbar) (to further capture “release resistant” water (RRW)). Using this data, a ratio of “easily extractable water (EEW),” generated as a first aliquot in RVS method(s) (“Aq1”) to total or sum water (taking into account water released by application of additional vacuum pressure using the RVS method(s), the RRW) was calculated. A ratio calculated in this manner may have a value ranging from 0 to 1, where “0” means that no EEW was measured, and all of the water present was RRW, suggesting that the measured water was relatively difficult to release from the corresponding drill cuttings sample. Conversely, a value of 1 means that all of the water measured was EEW, while none of the water measured was RRW, suggesting that the measured water was relatively easy to release from the corresponding drill cuttings sample. This data was plotted (scatterplot) against the relative positioning (depth; hung on the Morrow for each well) of the samples for each of the two wells and is presented in FIG. 1. FIG. 1 provides an overlay of the data from P123 and P525 on the same graph.

The results in FIG. 1 show that there is significant overlap in the pattern between the ratio of water calculated to be present in the drill cuttings from the two different wells. See, e.g., specifically depths identified as being from 0 ft to about +300 ft and, e.g., from about −900 ft to about −350 ft, where the water ratio(s) significantly overlap even though the ratio(s) within each such depth section are different. These results suggest that even though the two wells were drilled far apart in time: one in 1941 (P123) and one in 2020 (P525), some characteristics of rock materials within defined sites may remain, e.g., do remain, at least substantially the same over significant period(s) of time. In certain respects, one or more characteristics of rock materials collected from wells may not be suitable indicators of geographic change within a defined area over time. Further, in certain respects, one or more characteristics of rock material(s), e.g., drill cuttings sample(s) collected from wells, may not be suitable indicators of at least certain elements of field history.

Regarding FIG. 1, it can be noted that the scale and absolute range for the y-axis for the two wells is/are different. Without being bound to theory, such disparity may be attributable to differences between the age of the drill cuttings collected or, e.g., differences between the technology (ies) used to collect the drill cuttings of each well, including the size of the rock pieces in the drill cuttings collected from each well, etc.

The amount of rock volatile CO₂ (EERVCD) in the drill cuttings samples described above was also quantitatively measured and reported in nanomoles for each cutting.

The data for each drill cutting sample, sample depth (provided as Depth Hung on Meramec, in feet), and rock volatile CO₂ (in nanomoles) were scatter-plotted with an overlay of the data from P123 and P525 on the same graph. This data is provided as FIG. 2A. Note that the P123 drill cuttings were approximately 80 years old at the time they were analyzed, again, not having been protected environmentally during such period.

As is apparent in FIG. 2A, no drill cutting samples were assessed for P123 at both the most shallow and deepest locations represented by the graph, as the focus was placed on zone(s) of interest based upon prior work and for which supplementary data were available. Similarly, no drill cutting samples were available for P525 at locations from about +250 ft to about +100 ft and at locations from about −1,050 ft to about −1,400 ft. Locations identified as about +450 ft to about −800 ft represent locations at which samples for both wells were available and were thus analyzed. Across certain spans, no drill cuttings were available for analysis, as cores were taken from these spans.

The data for both P123 and P525, shown in FIG. 2A, demonstrate that there is some variability in rock volatile CO₂ from location to location, but general mean and median values can be observed. For example, in P525 from about +1,200 ft to about +500 ft, drill cuttings comprise mean and median rock volatile CO₂ amounts of about 70 nanomoles with a range of about 40 nanomoles to about 1,250 nanomoles.

Additionally, within the well depths where data are available for both P123 and P525, e.g., at about +450 feet to about −800 ft, there are spans where there is significant overlap in the amount of rock volatile CO₂ identified in the samples from the two wells-see, e.g., locations from about −150 ft to about −500 ft. This is a region known as “below St. Louis.” Note that data/results referencing “below St. Louis” include samples from the St. Louis region itself. There are also spans where there are significant differences in the amount of rock volatile CO₂ identified in the samples from the two wells, e.g., from about +450 ft to about +150 ft. This is a region known as “above St. Louis.”

The “below St. Louis” region of the wells is located below the deepest producing formations in the Patterson Site/Field. The data from “below St. Louis” (again, including data from samples from the St. Louis region itself) were plotted in FIG. 2B for both P123 and P525. FIG. 2B shows the amount of rock volatile CO₂ (in nanomoles) in 10 nanomole bins (“bins,” as identifiable in the figure(s), refers to groups or ranges, wherein, e.g., 0-10 nanomoles is a first bin, 11-20 nanomoles is a second bin, and, e.g., 151-160 is another bin, etc.) were plotted on the x-axis against the normalized frequency of occurrence for the amounts in each such bin on the y-axis, which ranged from 0 to about 0.14. As is illustrated by FIG. 2B, the data clearly show a strong overlap (fitted near-normal curve, mean, median, maximum normalized frequency) for both P123 and P525.

The “above St. Louis” region of the wells is located above the deepest producing formations in the Patterson Site/Field. The data from “above St. Louis” were plotted in FIG. 2C for both P123 and P525. FIG. 2C shows the amount of rock volatile CO₂ (in nanomoles) in 10-nanomole bins (see description of bins above) plotted on the x-axis against the normalized frequency of occurrence for the amounts in each such bin on the y-axis, which ranged from 0 to about 0.22 for P525 and 0 to about 0.15 for P123. As illustrated by FIG. 2C, the data clearly show strong differences (fitted near-normal curve, mean, median, maximum normalized frequency) for both P123 and P525. As an example, the peak (mean) CO₂ amount for P525 is about 75 nanomoles of CO₂, whereas the peak (mean) rock volatile CO₂ amount for P123 is about 160 nanomoles of CO₂-more than twice the amount in P525. As another example, the distribution of the amounts of rock volatile CO₂ for P525 ranges from about 40 nanomoles to about 110 nanomoles (representing a range of about 70 nanomoles), whereas the distribution for P123 ranges from about 80 nanomoles to about 225 nanomoles (representing a range of about 145 nanomoles), about twice the width of the distribution for P525.

These data suggest that the amount of rock volatile CO₂ in the assessed Patterson field has decreased in formations at or above the deepest producing petroleum formation (in the “above St. Louis” region) due to activity in the field between the time that P123 drill cuttings were collected in 1941 and when the P525 drill cuttings were collected in 2020. This change in rock volatile CO₂ is illustrative of one aspect of the field history. It is unclear from this particular analysis/Example what has or may have caused the decrease in rock volatile CO₂. Numerous factors are possible, such as, but not limited to, a pressure measurement affecting factor (PMAF being present) such as, e.g., petroleum production, water flooding since 2010, possibly biogenic activity, or, e.g., loss due to seal loss/break in a well or via a fault (e.g., that was crossed by one or more borehole environments) which may have each alone or in combination played a detectable or significant role in such a decrease.

However, where there has been no interference, e.g., no production activity and, e.g., no water flooding, e.g., no apparent PMAF present, in formations at or below the deepest producing formation (in the “below St. Louis” region, data for which as noted above included the St. Louis region itself), the amount of rock volatile CO₂ and the distribution of the amounts of rock volatile CO₂ in both P123 and P525 remain very similar.

In one aspect, this Example demonstrates that the amount of rock volatile CO₂ in drill cuttings is sufficiently robust to withstand exposure to unconditioned atmospheric temperature, pressure, and humidity for, e.g., at least nearly 80 years.

These results further suggest that even though the two wells were drilled far apart in time: one in 1941 (P123) and one in 2020 (P525), while one or more characteristics of rock materials within defined sites may remain, e.g., do remain, at least substantially the same over significant periods of time, certain other at least one or more characteristics, such as, e.g., the amount of rock volatile CO₂, can change. In certain respects, one or more characteristics of rock materials collected from wells, such as, e.g., the amount of rock volatile CO₂, may be suitable indicator(s) of geographic change within a defined area over time. Further, in certain respects, one or more characteristics of rock materials collected from wells, such as, e.g., the amount of rock volatile CO₂, may be suitable indicators of at least certain elements of field history.

Example 2

This example demonstrates that the amount of rock volatile carbon dioxide (CO₂) present in rock material samples, such as, e.g., drill cuttings, such as, e.g., easily extracted rock volatile CO₂ (EERVCD) measured in drill cuttings, is correlated with pressure at the location from which the rock material samples (e.g., drill cuttings) were collected, at the time at which such materials were collected. More specifically, this example illustrates how pressure in a formation, as measured by technology such as DST, correlates with the amount of rock volatile CO₂ measured in drill cuttings, such as, e.g., easily extracted rock volatile CO₂ (EERVCD) measured in drill cuttings, as, e.g., determined via rock volatile stratigraphy (RVS) method(s).

This example, at least in part, further demonstrates how pressure in different formations within a borehole environment can be measured based on quantitatively measured rock volatile CO₂ (easily extracted rock volatile CO₂ (EERVCD) data (quantitative data, QD) present in drill cuttings. This example demonstrates that pressure within a borehole environment can be measured based on the quantitative measurement of rock volatile CO₂ in drill cuttings, even when such drill cuttings are analyzed at least about 30 years after collection, and when such drill cuttings have not been stored during such period in a controlled environment.

The Branine 1-3 well (“B13,” or “Well 3”) was drilled in 1992 in the Hartland Field. The Hartland Field is located near the Patterson Field, previously described in Example 1.

The Hartland Field was first discovered in 1986, with oil being found initially in Mississippian lime with subsequent discoveries in the Morrow formation (1989), Lansing/Kansas City formation (1990), and the Marmaton formation (1990). Notably, unlike the Patterson Field, the Hartland Field has not undergone Enhanced Oil Recovery (EOR); whereas, in the case of the Patterson Field, water flooding, an exemplary pressure measurement affecting factor (PMAF) as described elsewhere herein, was implemented (described above in Example 1).

When B13 (Well 3) was drilled, Drill Stem Tests (DSTs) were conducted to measure the pressure (in psi) in 3 formations within the well: the Lansing formation (two measurements in the E and I subunits), the Marmaton formation, and the St. Louis formation. Each Drill Stem Test (DST) typically recorded the pressure over segments of about 20 feet to about 40 feet of the borehole environment.

At the time the B13 well was drilled, drill cuttings samples were collected from throughout much of the borehole, including locations adjacent to where the DSTs were conducted. That is, at the time the B13 well was drilled, a plurality of drill cuttings samples was collected, including from specific locations on either side of, e.g., that bracketed, the locations at which the DSTs were conducted. Analysis herein focuses on data collected from drill cuttings samples collected from the specific DST location and a zone (to the extent possible) about +/−50 ft from such locations. Each drill cutting sample collected represented about a 10-foot segment of the borehole.

The specific location(s) of the DSTs and the drill cuttings samples were both identified relative to the depth from the surface at which they were measured and collected, respectively, with the higher the depth value representing a deeper location (further from the Earth's surface) from which the sample was collected, or the test was conducted.

About 10 to about 20 drill cuttings samples, corresponding to specific locations about 50 feet above and below the specific location where the DST was measured, were analyzed about 30 years after their collection, having not been stored under strict, environmentally controlled conditions in the intervening period. Using the rock volatiles stratigraphy (RVS) methodology previously described (elsewhere herein and in the Prior Smith patents), the rock volatile CO₂ in each of the drill cuttings samples was quantitatively measured. Conditions of the RVS methodology employed included use of a 20-mbar vacuum extraction, characterizing the rock volatile CO₂ measured as easily extractable rock volatile CO₂ (EERVCD), and the quantitative data (QD) associated therewith as EERVCD QD. The amount of easily extractable rock volatile CO₂ measured (EERVCD QD) was recorded and reported in nanomoles.

These data for both the DST (in psi) and rock volatile CO₂ (more specifically, the EERVCD QD) (in nanomoles) were plotted on the x-axis, and the location (depth in feet) from which samples or DST measurements were collected on the y-axis. See FIG. 3A.

As is apparent in FIG. 3A, the individual values for the amount of rock volatile CO₂ (in nanomoles) show significant variability from cutting sample to cutting sample.

However, when the mean or median values of rock volatile CO₂ (in nanomoles) are plotted relative to pressure measured by DST (in psi) for the corresponding location, a strong correlation is established, with a linear model providing an R2 value of 0.8706. See such data presented in FIG. 3B.

Note that both FIGS. 3A and 3B indicate measurement of rock volatile CO₂ in aliquot 1 (“Aq1”) of the RVS method. This aliquot reflects the application of the low extraction force, gentle vacuum extraction (here at 20 mbar) described in this Example and which is further described in the Prior Smith patents.

Notably, the pressure in the borehole did not uniformly increase as a function of increasing borehole depth. As such, depth can be ruled out as confounding the rock volatile CO₂ amount (QD).

The results of Example 2 first establish that rock volatile CO₂ present in rock material samples, e.g., drill cuttings samples, collected from a location within a borehole, correlates with the pressure of such location at the time when such samples were collected. This example further demonstrates that this is true even when significant time has passed between when such samples were collected and when such CO₂ amount(s) within the samples are analytically determined. The results demonstrate that the higher the rock volatile CO₂ (e.g., EERVCD QD) present in the rock material samples, the higher the pressure present at that location at the time the rock material samples (e.g., drill cuttings) were collected.

Accordingly, Example 2 demonstrates that when a plurality of rock material samples, e.g., drill cuttings samples, e.g., samples collected from different specific locations within a single well, such as, e.g., from different formations within a single well; or, e.g., when a plurality of rock material samples (e.g., drill cuttings) are collected from different wells, and such samples are analyzed for the amount of rock volatile CO₂, e.g., EERVCD QD, the relative amounts of rock volatile CO₂ measured therein is indicative of, provides a measurement of, pressure at the locations from which the samples were collected. Accordingly, such information can be used to target areas of expected higher pressure, e.g., targeting such higher-pressure locations for petroleum production exploration; or, e.g., such data may be informative to/for other exploits, such as, e.g., carbon sequestration endeavors, as higher pressure may indicate a stability in such areas suitable for such endeavors. This is discussed further below. Additionally, low-pressure environments identified as stable by method(s) herein may be identified by such inventive method(s) as potential environments for carbon sequestration endeavors. For example, environments (e.g., zones) identified as having a pressure lower than would be expected based on the hydrostatic gradient may be suitable for holding a higher amount of injected carbon dioxide than a normally-pressured zone that is in line with the hydrostatic gradient.

In certain further aspects, the results of Example 2 establish a scale of the relationship between carbon dioxide and pressure; that is, in certain aspects, they provide a quantitative analytical model (QAM). In certain aspects, the QAM reflects the relationship between carbon dioxide and pressure according to a number of pressure indicative or pressure measurement affect(ing) factors, each relevant to the establishment of such a QAM, such as, e.g., play, formation, specific location, type of rock, local activity (e.g., EOR activity, biogenic activity, other drilling activity, and the like), etc., which may be accounted for by the incorporation of pressure measurement affecting factor (PMAF) quantitative data (QD), PMAFQD, as is described in other Examples and disclosure herein.

In certain aspects, once such a relationship is determined, e.g., once such a QAM is available, the QAM can be used to measure the pressure at a location by measuring the amount of rock volatile CO₂, e.g., EERVCD, to obtain rock volatile CO₂ QD, e.g., EERVCD QD, in rock material samples, and measuring the pressure according to the amount of CO₂ present. That is, in aspects, this Example demonstrates the establishment of a quantitative analytical model (QAM) for pressure. Reliability of such a measurement, e.g., the accuracy of such a measurement, may in aspects be dependent upon a number of comparability factors, such as, e.g., how close in distance any rock material samples tested were originally located from the area from which samples used to establish the QAM were derived; if formations are shared by the two sets of samples; how much time has passed between the time the scale was developed and when the additional rock material samples were collected; biogenic activity at the site of the new samples collection; the similarity (or differences) between the type of rock used in the establishment of the QAM and that of the new rock material samples, and others. Accordingly, in aspects, the higher the degree of overlap of one, some, most, generally all, or all comparability factors, the better (more accurate) measurement of pressure the QAM may offer (the more reliable the QAM may be as a measurement tool) or, stated another way, the more reliable the pressure measurement the QAM may provide. Alternatively, in aspects, the lower the degree of overlap of one, some, most, generally all, or all comparability factors, the poorer the accuracy of pressure the QAM may offer (the less reliable the QAM may be as a measurement tool). Stated another way, the lower the degree of overlap of comparability factor(s), the less likely the QAM may be to provide highly reliable pressure measurement(s).

According to certain aspects, Example 2 demonstrates that when, e.g., two or more direct pressure measurements, e.g., as obtained by DST, are available and a QAM such as that demonstrated here is established, it is as if a direct pressure measurement had been taken at all locations between those represented by the QAM. Thus, QAMs demonstrated here provide a plurality of, e.g., an essentially infinite number of, pressure measurements. As such, fewer, or, in certain circumstances, no direct pressure measurements by expensive and time-consuming technology (ies) (such as, e.g., DST) are required for characterizing a particular site, formation, or, e.g., well.

Further, in certain respects, Example 2 demonstrates that relative pressure, as determined by relative rock volatile CO₂ amount(s) in rock material samples (such as drill cuttings samples), can be compared across formations. In certain respects, this Example demonstrates that where the samples come from is irrelevant. In certain respects, such comparison(s) can be made using samples collected from different wells, e.g., that the rock volatile CO₂ amount is location-specific and can be interpreted (and associated decisions made using such interpretation) accordingly.

In certain further aspects, the results of Example 2 establish that the pressure in a borehole, at the time the borehole is or was drilled, can be, e.g., obtained by one method, e.g., in this case by DST, or measured by a different methods, e.g., by quantitative rock volatile CO₂ measurements, such as, e.g., by quantitative rock volatile CO₂ measurements of drill cuttings samples, e.g., by quantitative rock volatile CO₂ measurements of drill cuttings samples obtained via analysis using the RVS methodology, e.g., employing a 20 mbar vacuum extraction—e.g., by obtaining EERVCD QD. The results further show that the pressure can be measured in different formations within the same borehole and that the drill cutting samples used in the analysis can be analyzed after about 30 years, or perhaps (and expectedly) even longer, after they were initially collected, without the samples having been subsequently stored in strict temperature-, pressure-, or humidity-controlled environment(s) (or environments where any combination of temperature, pressure, and humidity are controlled). The results of Example 2 demonstrate that pressure within a well, and across formations, can be measured by the quantification of rock volatile CO₂, e.g., EERVCD, in drill cuttings collected at the time the well was drilled, but analyzed many years later.

These results further demonstrate a mechanism for analyzing the change in a geographical location as it relates to pressure over time. Comparing rock volatile CO₂, e.g., EERVCD QD, in rock material samples, e.g., drill cuttings samples, collected at the time a well was drilled, with rock volatile CO₂, e.g., EERVCD QD, in rock material samples, e.g., drill cuttings samples, collected from a newer, more recent well or a well currently being drilled within the same site, play, formation, etc., or combination(s) thereof, may provide insight into changes in pressure within the location over time which may inform decisions about the suitability of such location(s) for certain technological endeavors such as, e.g., CCS/CCUS.

Example 3

This example demonstrates how pressure in a formation in a field can be measured based on a ratio of the quantitative amounts of rock volatile CO₂ and water measured in rock materials.

As was described in Example 1, the Patterson 1-23 well (P123, Well 1) was drilled in the Patterson Field in 1941, and pressure measurements were made by DST in the Morrow Sand formation within the well. Subsequently, three additional wells were drilled in the Patterson field, and pressure measurements were obtained by DST in the Morrow Sand formation within each well at the time each of these wells were drilled: Patterson 1-31 (1991) (“P131,” “Well 4”), Patterson 3-25 (1995) (“P325,” “Well 5”), and Patterson 1-24 (1996) (“P124,” “Well 6”). Each of the four wells is located within about 3 miles of one another, and in one case, P124 and P123 are within about ½ mile of each other.

As was the case for P123, drill cuttings samples were collected from specific locations within each of the subsequent wells that included the specific locations at which the pressure measurements by DST were made in each well (or, e.g., adjacent thereto, e.g., bracketing either side of such location(s)). Subsequently, the drill cuttings samples were analyzed using the RVS methodology, previously described and further described in the Prior Smith patents, comprising application of a low extraction force gentle vacuum (here, extraction of 20 mbar, to obtain Aliquot 1 (“Aq1”)), cryotrapping of condensable volatile compounds, controlled warming, and quantitative measurement by mass spectrometry.

Initially, unlike in Example 2, results did not demonstrate a clear correlation between the amount of rock volatile CO₂ (in nanomoles) and the pressure as determined by DST (data not shown).

Based upon existing knowledge, a pressure measurement affecting factor indicator (PMAFI), e.g., a quantitative proxy (representing data quantity(ies), DQ) for such a PMAF (a PMAFIDQ) (elsewhere PMAFIDQ may be presented as PMAFDQ and uncontracted should be considered interchangeable, where it is understood that a PMAF must be quantified to be presented as data quantity(ies)), was determined to be potentially useful for improving upon the quantitative analysis model (QAM) to achieve an enhanced QAM (EQAM). Here, the amount of rock volatile CO₂ (in nanomoles) was normalized by the amount of easily extractable water (EEW), with the amount of EEW representing PMAFIDQ applied to the model.

When the amount of rock volatile CO₂ (in nanomoles) was normalized within the samples from each of the Patterson field wells by the amount of easily extracted water (EEW) (presented as counts) in the drill cuttings samples corresponding to the location at which the pressure measurement was obtained by DST, a stronger correlation was observed. That is, when the rock volatile CO₂, more specifically here the easily extracted rock volatile CO₂ (EERVCD) amount, the EERVCD DQ, was normalized with a PMAFIDQ—that being the quantitative amount of EEW (EEW DQ), the result was an enhanced QAM (EQAM). See FIG. 4, where the ratio of rock volatile CO₂:easily extracted water (nanomoles/counts) is plotted on the y-axis against the DST pressure measurement (psi) on the x-axis for each of the four wells. The linear quantitative analytical model (QAM)—that is, the enhanced quantitative analytical model (EQAM) provided a correlation coefficient, R², of 0.9427. Note that FIG. 4 incorporates data from samples collected from a single formation within four different wells, each having experienced different amounts of production over time in the field.

The data of Example 3 demonstrates that the application of PMAFIDQ can increase the reliability of QAM(s). Further, this Example demonstrates that QAM(s) herein respond to changes in associated data on time scales of years in the field. The data of Example 3 demonstrates that the QAM holds across wells, within the same formation.

The same utility in terms of relative rock volatile CO₂ analysis, QAM establishment and use, impacts of QAM accuracy, and the like described in Example 2 apply here in Example 3 and are not repeated for the sake of brevity.

One may note that in FIG. 4, the four represented wells were drilled at different points in time, with the well drilled most recently furthest to the left and the well drilled the longest ago being the well located the furthest to the right. A very small slope is identified (0.0008). Because of the chronology of the wells graphed, such a representation indicates a relationship between time and pressure within this formation. In certain aspects, methods herein may be predictive of the future pressure of/in a well or other unit of area, e.g., in a new well, may comprise generating or using an established graph. In certain other circumstances, such an identified slope may be far larger (e.g., sizably different). Because the rock volatile CO₂ or rock volatile CO₂:water ratio compared to pressure can be plotted for wells drilled at different time periods, identifying a slope of change can provide a tool for predicting pressure in a well drilled in the future. This may be applicable to other model(s) described herein as well, such as, e.g., model(s) which incorporate one or more salinity-indicative compound(s). Further, these method(s) and associated QAM(s) can be applied to and within different zones of a well. Method(s) and QAM(s) herein are not relevant only to, e.g., different formations or layers of rock, but rather, can be applied to the same layer of rock. This is relevant to, e.g., unconventional lateral well drilling/exploration related activity(ies).

The theoretical basis for the solubility of CO₂ in water, and the solubility of CO₂ in oil, each as a function of pressure (and other considerations), has been established in the art. This has been used in practical engineering applications for things such as, e.g., enhanced oil recovery efforts and saline aquifer injection (relative to CCS endeavors).

However, the approach as exemplified in Example 3 is the first to demonstrate that such a relationship (involving or based upon pressure quantitative rock volatile data (PQRVD)) can be mathematically modeled, e.g., used in the establishment or application of a QAM. Such modeling can be accomplished by, e.g., extracting volatiles from legacy drill cutting samples with, e.g., gentle vacuum, e.g., a low extraction force gentle vacuum (e.g., a 20 mbar vacuum), measuring the amount(s) of rock volatile CO₂ (EERVCD) and water (EEW) (using, e.g., the rock volatile stratigraphy (RVS) technology (ies)/methodology (ies)), and applying, e.g., linear regression or other appropriate and applicable fit model(s). That is, Example 3 demonstrates that a quantitative analytical model (QAM) can be created to measure pressure (psi) in a formation in a field as a function of the quantitative amount of rock volatile CO₂, e.g., EERVCD (e.g., EERVCD DQ) (in nanomoles) and water, e.g., EEW (e.g., EEW DQ) (as counts) in rock material samples (e.g., drill cuttings samples). This model is particularly useful in assessing how pressure has been changing in a field that has been affected by one or more associated condition(s) (e.g., production activity(ies), subsurface activity(ies), etc., or, e.g., possibly by other activity(ies) such as, e.g., enhanced oil recovery effort(s)). Further, Example 3 demonstrates that such model(s) are sufficiently robust to remain applicable for use with drill cuttings collected from the same formation from different wells at different times.

Example 4

This example demonstrates how a quantitative analytical model can be created to measure the pressure in one or more formations, here deep carbonate saline aquifers, when the quantitative amount of rock volatile CO₂, e.g., easily extracted rock volatile CO₂ (EERVCD) (in nanomoles) and water, e.g., easily extracted water (EEW) (in counts) are measured.

As was discussed in Example 3, DST pressure measurements were obtained in the Morrow Sand Formation in 4 different wells—P123 (Well 1), P131 (Well 4), P325 (Well 5), and P124 (Well 6), at the time such wells were drilled, ranging from 1941 to 1996.

Subsequently, in 2020, another well was drilled, KGS 5-25 (“K525,” or “Well 7”) in the same Patterson field, within a few miles of the aforementioned wells. The purpose of the well was to assess the viability of storing and sequestering CO₂ in one or more deep carbonate formations, Osage, Arbuckle, and/or Viola, each containing a saline aquifer. The Arbuckle formation was believed to have multiple prospective zones suitable for carbon capture and sequestration activity(ies).

The pressure was measured in each of these three formations, obtained as part of breakdown tests on these formations (before applying pressure during the breakdown testing). Accordingly, the breakdown testing pressure measurement data here were attained from breakdown testing varying from traditional/normal breakdown testing, representing an alternative method for obtaining direct pressure measurement(s). Rock material samples (drill cuttings samples) were obtained from each well, including samples from specific locations corresponding to the locations where the pressure measurements were made in each well (e.g., at, adjacent to, or bracketing such specific location(s)). The rock volatile stratigraphy RVS) methodology was used to analyze the rock material (drill cuttings) samples using a low extraction force gentle vacuum (here a low extraction force gentle vacuum extraction of 20 mbar) (represented as aliquot 1 (“Aq1”) of the method), cryogenic trapping of condensable volatile compounds, controlled warming of the cryogenic trap, and quantitative measurement by mass spectrometry.

In this particular case, a ratio of rock volatile CO₂ data quantities (DQ) (more specifically, EERVCD DQ):water DQ (more specifically, EEW DQ) was generated for each of the drill cuttings samples. Multiple locations were evaluated, and ratios were obtained for each. When the median values of the rock volatile CO₂:water (e.g., EEW) and the pressure measurements obtained by DST of the corresponding locations were added to the data presented in FIG. 4, a new linear quantitative analytical model (QAM) provided an R2 of 0.8588. See FIG. 5. This indicates a strong relationship between pressure in different formations in different wells within the same field and the quantitative ratio of rock volatile CO₂:water (e.g., EEW) in the drill cuttings from such formations and wells.

While deep carbonate formations within saline aquifers are not necessarily suitable for petroleum production, they are considered to be potentially viable for carbon capture and sequestration (CCS). In this regard, if a pressure measured early in the history of a location, as determined by, e.g., DST (or other methodology), is significantly higher than the measured pressure made using the QAM (obtained by the measurement of at least rock volatile CO₂ or, e.g., at least rock volatile CO₂ and water (e.g., a least EERVCD and EEW), with or without other PMAFIDQ as described herein, this may suggest that the pressure has decreased by one or more mechanisms such as seal failure in a well in the same field and in fluid communication with this formation or the CO₂ travelling up a fault in fluid communication with this formation. In either case, such an outcome would suggest that such a formation is not suitable for CCUS.

Conversely, the pressure measurements obtained by DST and the ratio of rock volatile CO₂:water (e.g., EERVCD DQ:EEW DQ) in deep formations that have most likely not been disturbed through petroleum exploration and production, and therefore represent a stable baseline, may be used to measure the pressure in another formation in one or more borehole locations which are being considered for petroleum exploration and production. If the measured pressure in these locations deviates from that measured at a different point in time, it may suggest that activity has occurred such as, e.g., previous production in the field, EOR activity(ies), seal failure, or, e.g., fault rupture, all parameters pertaining to field history, which may inform whether such a location is suitable for further exploration or production.

The same utility in terms of relative CO₂ analysis, e.g., rock volatile CO₂ analysis, QAM establishment and use, impacts of QAM accuracy, and the like, described in Examples 2 and 3, apply here in Example 4 and others and are not repeated for the sake of brevity.

Discussion of the impact of deep saline aquifers and their relevance to the model is further discussed in Example 5.

Example 5

This example further demonstrates the robustness of the quantitative analytical model(s) (QAM(s)) discussed herein. Example 5 demonstrates that the model is agnostic with regard to well, formation, field, and time.

As has been discussed in previous Examples, the Patterson field and Hartland field contain petroleum-producing formations. While the Patterson field has undergone EOR activity(ies) since 2010, specifically water flooding (representing a known PMAF), the Hartland field has not undergone any such EOR activity(ies).

As was discussed in Example 4, there is a strong correlation between directly measured pressure measurements, e.g., those obtained by DST, and the quantitative ratio of rock CO₂ (e.g., easily extractable rock volatile CO₂ (EERVCD), e.g., EERVCD DQ:water (e.g., EEW, e.g., EE WDQ) in and across numerous formations and in and across numerous wells drilled over long periods of time.

As was shown in Example 2, the Branine well, B13 (Well 3), showed a strong correlation between the amount of rock volatile CO₂ (in nanomoles) and the DST pressure measurements.

Here in Example 5, an adjustment to the B13 data is made by measuring the amount of water, more specifically EEW, in the same drill cuttings within which the rock volatile CO₂ was measured, and further creating a ratio of rock volatile CO₂ (more specifically, EEVCD DQ):water (more specifically, EEW DQ) (as nanomoles/counts) for such drill cuttings samples. These data are then included with the data previously presented in FIG. 5, resulting in FIG. 6.

FIG. 6 then reflects data from samples collected from a single formation (Morrow) but from different wells within the formation, within the Patterson field (left four circles); samples collected from four different formations within a single, (different) well within the Patterson field (right four circles); and samples from four different formations within a single (different) well from a different field, the Hartland field (triangles).

When these data are presented together, a new quantitative linear model (QAM) provides an R2 of 0.8512 (See FIG. 6).

This data demonstrates a strong correlation between the ratio of rock volatile CO₂ (more specifically, EEVCD DQ):water (more specifically, EEW DQ) and pressure, e.g., pressure as provided by the DST pressure measurements. These results demonstrate that linear mathematical models, e.g., linear quantitative analytical models (QAMs), can be created based on data from one or more (a plurality of) formations, within one or more wells, located within one or more fields. As such, these data further demonstrate the robustness of the model(s) disclosed herein.

These data yet again, in one aspect, provide a QAM (an exemplary QAM) which may be used to measure the pressure in one or more other locations, e.g., one or more locations within the field and further, possibly, one or more other locations different field(s), where the pressure has not been directly measured by technology (ies) such as DST, as a function of the rock volatile CO₂:water ratio.

In certain respects, in this Examples and others, differences between a measured pressure and a measured pressure from a different period of time (e.g., a pressure measured by DST or similar technology (ies) may be attributable to field history, previously discussed, which may inform as to the suitability of the location, e.g., formation, where there are discordant measured pressures for further petroleum exploration and production or carbon capture and sequestration, such as CCU or CCUS.

It is notable, e.g., with regard to the comparison of the data in FIG. 6 with, e.g., the data of FIG. 4, that the scales of the graphs reflecting the model (QAM) are not identical. Accordingly, a comparison of the linear equations and associated R2 values of the data in each graph can provide a more insightful understanding of the data as compared to, e.g., a visual comparison of the data.

Example 6

Example 6 further extends the utility and characterization of the QAM(s) described herein, by addressing pressure indicating factor(s), such as, e.g., salinity, which may impact model performance, or which may dictate for what geologic material(s) QAM(s) provide highly reliable pressure measurement(s). Example 6 demonstrates that the use of associated condition data (ACD) may (1) indicate opportunity (ies) to incorporate pressure indicating factor indicator(s) (PMAFI(s))—more specifically, to incorporate PMAFI quantitative data (PMAFIDQ) or (2) indicate opportunity (ies) to eliminate certain geologic material sample(s) from use with particular QAM(s). In one aspect, by way of incorporating PMAFIDQ, this Example demonstrates mechanism(s) for enhancing QAM(s), to establish enhanced quantitative analytical model(s) (EQAM(s)). In one aspect, by way of, i.a., using ACD to identify geologic material samples that are not appropriate for use with particular QAM(s), the reliability of QAM(s) is enhanced by way of data exclusion.

Salinity, as has been discussed herein, may be characterized as a pressure measurement affecting factor (PMAF), by way of its impact on CO₂ solubility in water. To assess salinity, a proxy for such a PMAF is required. Herein, sulfate ((SO₄)²⁻) is treated as a proxy for salinity. It is understood that its correlation to salinity may be limited to a weak, oppositional correlation (unlike, e.g., sodium or chloride ions, which would demonstrate a strong direct correlation). That is, sulfate can operate as a pressure measurement affecting factor indicator (PMAFI) of salinity. However, if using mass spectrometry as a rock volatile quantification tool, sulfate may be challenging to measure. “SO,” here representing a sulfate proxy, e.g., sulfur monoxide or ion thereof or an SO fragment of the compound sulfate formed by way of analysis by mass spectrophotometry (e.g., being produced for example during ionization of sulfate released from a rock material sample during analysis) as is discussed elsewhere herein, can be used as an indicator of sulfate which may be cleaner to measure (may be more clearly measured) via technology (ies) such as mass spectrometry. SO, may be described in aspects as a “proxy of a proxy” or an indicator of the PMAFI sulfate (e.g., used as a pressure measurement affecting factor indicator, PMAFII), the PMAFI sulfate being used as a PMAF of salinity. Herein, the use of “sulfate/SO” is commonly used to reference this PMAFI/PMAFII.

See FIG. 7. Referring back to the P525 well, located within the Patterson field, sulfate/SO was additionally measured in the drill cuttings samples collected therefrom (such drill cuttings samples having been collected from three different formations within the P525 well) to obtain sulfate/SO DQ. Again, the RVS methodology with a low extraction force, gentle vacuum was used for this analysis. Here, the sum of the low extraction force gentle vacuum sulfate/SO quantity and the high extraction force gentle vacuum sulfate/SO quantity was used in the analysis (e.g., in the development of the EQAM).

The sulfate/SO results (DQ) for drill cuttings samples were plotted against the rock volatile CO₂:water ratio data for the same drill cuttings samples from the three different formations in the P525 well, and a strong correlation of 0.8951 was identified (see FIG. 7).

The results presented in FIG. 7 demonstrate that sulfate/SO correlates with the rock volatile CO₂:water ratio (e.g., EERVCD DQ:EEW DQ ratio) in the exemplified formations across two different fields (R²=0.8512). The comparison of one rock volatile (or ratio of rock volatile(s)) to one or more other rock volatile(s) or ratios comprising such one or more other rock volatile(s) as performed as step of this Example can be one aspect of the technology (ies) herein and, e.g., may lead to insight(s) into how to improve a QAM to establish, e.g., an EQAM.

Moving now to FIG. 8, it was previously demonstrated herein that there is a strong correlation between the ratio of the rock volatile CO₂ (e.g., EERVCD DQ):water (e.g., EEW DQ) and pressure across wells, formations, fields, and, e.g., rock types/sizes (e.g., whereby drill cuttings samples generated by use of differences in drilling technology (ies) and drill bit(s) across time), etc. See, e.g., FIG. 6.

FIG. 8 provides the data presented in FIG. 6 but excludes the data from the deep saline aquifers in the P525 well. This step reflects the consideration of differences in one or more pressure measurement affecting factor(s) (PMAF(s))—such as, e.g., salinity-between locations from which material samples are collected. This is discussed in more detail below. In certain aspects, data associated with one or more location(s) where PMAF(s) is/are present may be referred to herein as, e.g., associated condition data (ACD). ACD reflects differences in geologic material(s). For example, given a set of 6 wells, where the PMAF “high salinity” exists for two wells, which differentiates the condition of those two wells from the condition of the other four wells, the high salinity status of the two wells establishes the data associated with such condition-differentiated wells as associated condition data (ACD). This ACD may be utilized in multiple ways, in, e.g., different scenarios. First, ACD may be contemplated in, e.g., the generation of QAM(s), where it may identify certain PQRVQD (with or without PMAFQD) associated with samples that are not appropriate for inclusion in a QAM, and thus such PQRVQD (with or without PMAFQD) may be eliminated from QAM(s) to establish EQAM(s). Second, ACD may be contemplated in, e.g., the consideration of whether or not PQRVQD (with or without PMAFQD) can be used to measure pressure using a particular QAM; e.g., whether or not a QAM is a good fit for use in determining pressure given some or all of the PQRVQD (with or without PMAFQD). In certain instances, ACD may be used to establish whether a single or multiple QAMs are appropriate for use in determining pressure from PQRVQD (with or without PMAFQD).

Here in this Example, the ACD was considered, and upon consideration, it was determined that certain geologic material samples were sufficiently different from others so as to indicate that their removal may provide a more reliable pressure measurement for specific locations represented by remaining geologic material samples.

Upon the exclusion of data related to such identified geologic material samples, as is shown in FIG. 8, the correlation between the rock volatile CO₂:water ratio and pressure improved for the Patterson (R²=0.9427)—thus establishing higher reliability pressure measurement provided by the QAM for the Patterson field (while the correlation between the rock volatile CO₂:water ratio and pressure for the Hartland field remained the same (R²=0.8405) as the presence of the now excluded data was irrelevant to the Hartland field.). The combined data of the two fields, with the data from the deep saline aquifers in the P525 well removed, decreased (R²=0.745), thus decreasing the reliability of the QAM to predict pressure when the remaining data from the two fields remained combined.

While the evaluation of rock volatile CO₂ (e.g., EERVCD, e.g., EERVCD DQ) alone, or rock volatile CO₂ (e.g., EERVCD, e.g., EERVCD DQ) and water alone, work well and demonstrate a high correlation within fields using a particular QAM, when comparing fields within a single QAM, one can see that the correlation is indeed different; e.g., the slopes of the relationship between the quantitative rock volatile(s) (PQRV(s)) or adjusted value(s) thereof, e.g., value(s) accounting for one or more PMAFIDQ(s) such as, e.g., EEW, and pressure is different.

FIG. 8 is a reflection of FIG. 6, without the additional well comprising the 4 deep saline aquifers, the data from which is reflected by the far right 4 data points of FIG. 6. Again, when data from such a well is included, the R²=0.8512 for the combination of fields (FIG. 6); while with data from such a well excluded, the R²=0.745 for the combination of fields (FIG. 8). As shown, when the data from the fields (Patterson and Hartland) are combined, the correlation reflected in the graph of FIG. 6 appears strong; however, when the two fields are separated, it is apparent that the slopes of the correlations within the two fields are different and while each reflects a high confidence level, their difference is indicative of other element(s), e.g., potentially one or more associated conditions, being in play. That is, their difference reflects a potential difference in the geologic material in the two fields; there is something different about their respective ACDs. Further, the removal of deep saline aquifer data (removal of PQRVD associated with samples having a sufficiently different ACD) modifies (improves) the correlation within the applicable field.

The data in FIG. 8 suggests that something is different between the two fields. Further, the data of FIG. 8 and external data associated with the fields indicate that salinity may be playing some sort of role in the quantitative analytical model(s) (QAM(s)) disclosed here, and indicate that further improvement(s) on the model may be possible if salinity were considered.

As will be understood by ordinarily skilled persons, the solubility of CO₂ in water can be influenced by the salinity of the water, i.e., ionic strength or TDS.

Knowing this, Example 6 continues by exemplifying attempts to determine if an improved quantitative analytical model (QAM), e.g., an enhanced quantitative analytical model (EQAM), could be developed that not only contemplated the quantitative amount of rock volatile CO₂ and water (e.g., EERVCD DQ and EEW DQ) in rock material sample(s) (e.g., drill cuttings sample(s)), but also salinity as an additional variable and thus establish an EQAM model incorporating pressure quantitative rock volatile(s), e.g., rock volatile CO₂ (e.g., EERVCD), optionally pressure measurement affecting indicator(s) quantitative data, e.g., water (e.g., EEW), and salinity-indicative compound(s) (e.g., salinity-indicative rock volatile compound(s)).

Salts, including, e.g., salinity-indicative salts, such as, e.g., sulfate, may be considered for use in QAM(s) herein. However, salts, including, e.g., salinity-indicative salts, may be selected according to the analytical technology (ies) to be used to quantify them. Unfortunately, most salts are unsuitable as components that can be measured by certain technology (ies) such as mass spectrometry, e.g., mass spectrometry used in RVS technology (ies)/methodology (ies) and thus proxy(ies) thereof may be used. See discussion of sulfate/SO above.

Again, using the RVS methodology (ies), rock material samples (drill cuttings samples) were collected from various formations from within various wells within both the Patterson field and the Hartland field and subsequently analyzed. Sulfate/SO was analyzed in rock material (drill cuttings) samples from B13 collected from the Lansing formation (E and I), Marmaton formation, and the St. Louis formation therein. Additionally, sulfate/SO was measured from drill cuttings samples collected from the Morrow formation within P525, P124, and P131. See FIG. 9A, where the quantitative sulfate/SO amounts (counts) (sulfate/SO DQ) are plotted on the y-axis for each well and formation from which the corresponding rock material samples (drill cuttings samples) were collected.

FIG. 9A clearly indicates that there is a difference in sulfate/SO between the wells of the two formations.

FIG. 9B is essentially a repeated presentation of FIG. 8. Given the observation illustrated by FIG. 9A (described above), the sulfate/SO data from FIG. 9A were added to the data presented in FIG. 9B. This new (combined) data set is not shown.

When the quantitative sulfate/SO data were combined with the quantitative data from the representative wells shown in FIG. 9B (quantitative rock volatile CO₂:water ratio data), a new quantitative mathematical model (QAM) was established that provided an R² of 0.96 to 0.98 for the PQRVQD (with PMAFQD) (data not shown). This model demonstrates an even stronger mathematical relationship between quantifiable volatile compounds (and relationships thereof) and pressure when salinity-indicative compound(s) are included, establishing an enhanced quantitative analytical model (EQAM). Notably, the R² value(s) is/are not a fixed value because the model was generated by the LINEST/linest function in Excel. This function requires the input of various starting points to build a model, the model having a fixed R² based on the starting points. As the model uses two variables, CO₂:water (in nanomoles/counts) and sulfate/SO (counts) to measure pressure (psi), the model (EQAM) is not suitable for being displayed in a 2-dimensional format.

Regarding FIGS. 8 and 9A, it is not, in aspects, simply that salinity difference(s) explain the different trend lines between the Hartland and Patterson fields (e.g., as exemplified in FIG. 8). Looking only at the data from B13 in, e.g., FIG. 9A, it is notable that the data from Lansing “E” appears to be detectably or significantly different from that of the other three data points from that well (from that of Lansing “I”, Marmaton, and St. Louis). Evaluating the sulfate value in FIG. 9A, the Lansing “E” divergence from the trend of the well can, even within the B13 well, be related back to the difference in sulfate content. This reveals that method(s) and analysis(es) provided herein can also or alternatively identify differences in salinity between single formations within a single well (e.g., as exemplified between the Lansing “E” and the other formations tested in the B13 well). This information can be used in method(s) herein. This information, e.g., may in aspects be used to adjust QAM(s) to establish EQAM(s). In aspects, this information, e.g., may in aspects be used to adjust QAM(s) to establish EQAM(s) by, e.g., the inclusion or exclusion of data or by other mechanism(s) of EQAM development described herein.

Accordingly, the Examples provided herein demonstrate that in certain circumstances, pressure quantitative rock volatile data, e.g., rock volatile CO₂ data alone, provides a strong (e.g., highly reliable) measurement of pressure. In aspects, rock volatile CO₂ data alone can be used to obtain pressure measurements using a QAM. Further, the Examples provided herein demonstrate that in certain circumstances, rock volatile CO₂ and pressure measurement affecting factor(s) data, e.g., water measurements together, e.g., a rock volatile CO₂:water ratio (e.g., a rock volatile CO₂:easily releasable water ratio), are a strong (e.g., highly reliable) measurement of pressure. In aspects, applied in/arising in certain circumstances, rock volatile CO₂ and pressure measurement affecting factor(s) data (e.g., water measurements) together, e.g., as a rock volatile CO₂:water ratio (e.g., a rock volatile CO₂:easily releasable water ratio), can be used to obtain pressure measurements using a QAM. Still further, the Examples provided herein demonstrate that in certain circumstances, CO₂, water, and associated condition consideration, e.g., data associated therewith (PQRVD associated with ACD), e.g., a salinity indicator such as sulfate/SO, together form a quantitative analytical model (QAM) which provides a highly reliable measurement of pressure. The use of CO₂ and sulfate/SO, without water, may also provide a suitable model for measuring pressure. In aspects, ACD can be used to (1) build an EQAM (e.g., either by incorporation or omission of geologic material samples associated with particular ACD); (2) determine geologic material samples which may be used to reliably determine pressure using a particular QAM and which should be excluded to increase reliability of the QAM; (3) determine if, given a particular set of geologic material samples, whether pressure measurements for the specific locations associated with each respective geologic material sample can be reliably obtained from a single QAM or if multiple QAM(s) are more appropriate for the provision of highly reliable pressure measurement data. The developer further envisions temperature, given its relevance to carbon dioxide solubility, as being considered in QAM(s)/EQAM(s) development as a PMAF or PMAFI (or derivative or further indicator thereof) and incorporated into QAM(s)/EQAM(s) as PMAFIQD. Temperature may also or alternatively be contemplated as relevant to the identification of geologic material sample(s) appropriate for use with particular QAM(s)/EQAM(s) to obtain reliable pressure measurement(s).

The Examples herein, as exemplified by the specific compounds used in the Examples, demonstrate the existence and identification of both pressure-indicative compounds and condition-associated compounds, e.g., salinity-indicative compounds and, still further, the existence of quantifiable pressure measurement affecting indicator data. Further, these Examples demonstrate that quantitative analytical models can be established using one, some, or all of one or more pressure-indicative compounds, one or more condition associated compounds (e.g., salinity-indicative compounds), and one or more pressure measurement affecting indicator(s), whereby each of such constituents can be quantified and used to measure pressure in at least a portion of a geologic unit in lieu of direct measurement using one or more direct measurement technologies such as, e.g., DST.

Further, in combination, the Examples provided here demonstrate (1) how certain PQRV data may not provide a reliable model, (2) how selective removal of such data can improve a model based on a smaller data set of the overall PQRV data (also or alternatively how selective removal of certain material sample data can improve the reliability of a model for predicting pressure based on remaining material sample data), (3) how one may investigate potential additions to the model (here, by examining if there is a correlation between earlier PQRV or PQRV-derived data (RVCO2/RV water) and a candidate PMAF/PMAFI based on PMAFQD (sulfate/SO)) to determine if a candidate PMAF/PMAFI should be tested in a new putative analytical model, and (4) how a new putative analytical model (putative QAM) may be validated to provide an EQAM. Step (3) of the procedure described here reflects a step that can be used in the generation of a QAM in accordance with other aspects of this disclosure.

Example 7

This example demonstrates an exemplary procedure of an exemplary method provided by this disclosure. This example describes the evaluation of pressure quantitative rock volatile data (PQRVD) in a quantitative analytical model (QAM), possible enhancement of the model (e.g., to establish an enhanced QAM (EQAM)), and final application of the model to determine the pressure in at least a portion of a geologic unit (GU). This Example is illustrated in FIG. 10 herein.

FIG. 10 is a flow chart of the steps of an exemplary procedure of an exemplary method of this disclosure as described above.

The exemplary method starts with obtaining quantity(ies) of PQRVD(s) from geologic material(s) (GM(s)), such as, e.g., rock material(s)/rock material sample(s), such as, e.g., drill cuttings sample(s), collected from locations in a geologic unit (GU). Such quantities are referenced herein as pressure quantitative rock volatile data quantities (PQRVDQs). Further, the method comprises obtaining associated data regarding the condition(s) of the GM(s) and condition(s) of the GU or GU locations (GULs) that is/are relevant to determining the fit of the PQRVQs to existing PQRVQ/GU pressure (GUP) model(s) (QAM(s)) (such associated data referenced herein as associated condition data (ACD)).

Upon gathering PQRVQ(s) and ACD, the procedure comprises a step to pose the question of whether or not a quantitative analytical model (QAM) exists for quantifying GUP from the PQRVQs. If such a QAM does exist, the procedure comprises a step to consider whether or not the QAM provides highly reliable pressure measurement(s) (HRPM(s)), such that the model should be used to determine GUP. If the QAM does provide HRPM(s), the QAM is then applied to PQRVQ(s) to determine GUP.

If the QAM is determined not to provide HRPM(s), the method comprises a step to consider whether or not the inclusion of pressure measurement affecting factor (PMAF) or pressure measurement affecting factor indicator (PMAFI) data enhances the reliability of the volatiles-derived pressure measurements (VDPM) over (i.e., over and above/beyond) that of the QAM; that is, whether or not inclusion of PMAF or PMAFI data generates an enhanced QAM (EQAM). Exemplary pressure measurement affecting factor(s) (PMAF(s)) can include, e.g., things such as the presence of a fault, presence of past or present activity such as, e.g., water flooding, high salinity conditions, etc. (see description(s) elsewhere herein), such activity or condition(s) having an impact on pressure. Exemplary pressure measurement affecting factor indicator(s) (PMAFI(s)) can include, e.g., “proxies” of such PMAF(s) (representation(s)/representative(s) of such activity(ies)/condition(s)). For example, rock volatile water can be a PMAFI of the PMAF water flooding; sulfate/SO can be a PMAFI of the PMAF salinity condition(s), etc. If the inclusion of PMAF or PMAFI data, including, e.g., PMAFII data as applicable, generates EQAM, PMAF data, PMAFI data, PMAFII data, or combination(s) thereof are obtained and factored into the model as quantifiable data/data quantities (DQ), sometimes referred to herein as PMAFIDQ.

This step is optionally repeated, e.g., repeated until optimized given available PMAF/PMAFI data, to generate EQAM(s). Each EQAM is then evaluated to determine if the EQAM provides highly reliable pressure measurements. If the EQAM is determined to provide HRPMs, then the EQAM is applied to PQRVQ to determine GUP.

If the EQAM is determined not to provide HRPMs, the method comprises a step for evaluating if the selective removal of PQRVQ data provides an EQAM. Such selective removal can be done on any appropriate basis, such as, e.g., but not limited to, differences between locations from which rock material samples are collected (e.g., differences between wells, etc.), as is described elsewhere herein. That is, such selective removal can be based upon differences in ACD between location(s) represented by sample(s). If selective removal of PQRVQ data provides an EQAM, such data is selectively removed, and the model is reevaluated to generate an EQAM. This process is optionally repeated until all appropriate data is selectively removed. Upon removal, the EQAM is reconsidered to determine if the EQAM provides HRPMs. If the EQAM is determined to provide high HRPMs, then the EQAM is applied to PQRVQ to determine GUP.

Returning to the beginning of the exemplary method, if upon initial consideration it is determined that a QAM does not exist for quantifying GUP from PQRVQs after obtaining PQRVQs and ACD, the method comprises a step for considering whether or not a putative analytical model (PAM) exists for quantifying GUP from the PQRVQs. If such a PAM does exist, the PAM is evaluated to determine whether or not the PAM can act as a QAM for the PQRVQs and VDPMs. If the PAM can act as a QAM for the PQRVQs and VDPMs, then such a QAM is considered for its ability to provide reliable HRPMs, and the procedure continues as described above. However, if such PAM(s) do not exist, a direct pressure measurement for location(s) in the GU is obtained (such as, e.g., by DST or similar/equivalent technology (ies)/method(s)). These direct pressure measurement data, along with PQRVQs, are then used to develop PAM(s) in view of ACD. Such PAMs are then (subsequently) considered for their ability to act as a QAM for PQRVQs and VDPMs. If they can act as a QAM for PQRVQs and VDPMs, the QAM is considered to determine its ability to provide HRPMs, and, again, the process continues.

In certain respects, this Example demonstrates that once QAMs or PAMs are established, they can be used to determine highly reliable GUP measurements without the requirement for obtaining direct pressure measurements. Further, this Example demonstrates how QAMs or PAMs can be initially developed/established (e.g., using direct pressure measurements and PQRVD) and further iterated to obtain enhanced QAM(s) (EQAM(s)). Such QAM(s) and EQAM(s) can subsequently be used with PQRVQs, etc., to obtain highly reliable measurements. For example, this Example demonstrates how QAMs can be iterated, e.g., improved, by, e.g., assessing the inclusion of PMAFIDQ, the exclusion of data based upon ACD (e.g., removal of PQRVD associated with samples from geologic locations having sufficiently different ACD), or both, to establish EQAMs capable of providing highly reliable pressure measurement(s) (HRPM(s)). This Example still further demonstrates that the development of QAM(s) (e.g., highly reliable QAM(s)) can consider the incorporation of a single volatile or multiple, e.g., a plurality of volatiles. Finally, this Example demonstrates that the development of QAM(s) (e.g., highly reliable QAM(s)) can comprise evaluating which volatile(s) are applicable to varying scenarios, contemplating associated condition data in the development of QAM(s).

Example 8

This example demonstrates an exemplary procedure of an exemplary method provided by this disclosure. This example describes the analysis of candidate pressure quantitative rock volatiles (PQRVs) and associated pressure quantitative rock volatile data (PQRVD) to generate highly reliable model(s) for determining the geologic unit pressure (GUP) of at least a portion of a geologic unit (GU) from PQRVD, either alone or, e.g., in aspects in combination with other data such as, e.g., pressure measurement affecting factor (PMAF) data, pressure measurement affecting indicator (PMAFI) data, indicator(s) of PMAFI(s) (PMAFII(s)) or combination(s) thereof (present in a model as PMAFIDQ).

FIG. 11 provides a flow chart of the steps of such an exemplary procedure for analyzing candidate PQRVs and associated PQRV data (“PQRVD”) to arrive at a highly reliable model for determining GU pressure (“GUP”) from PQRVD.

The exemplary method begins with the measurement of easily extracted rock volatile carbon dioxide (EERVCD) in geologic materials(s) (GM(s)), such as rock material samples, e.g., drill cuttings samples. The procedure first considers whether the quantity of EERVCD measures pressure with high reliability. If the quantity of EERVCD is determined to measure pressure with high reliability, the EERVCD quantity (EERVCDQ) data (EERVCD DQ) is used to evaluate GU pressure.

If, on the other hand, the quantity of EERVCD is determined not to measure pressure with high reliability, pressure measurement affecting factor (PMAF) or pressure measurement affecting factor indicator (PMAFI) data (or, e.g., indicator(s) of PMAFI(s), PMAFII(s) data) are obtained.

As described in Example 7 and elsewhere herein, exemplary pressure measurement affecting factor(s) (PMAF(s)) can include, e.g., things such as the presence of a fault, presence of past or present activity such as, e.g., water flooding, high salinity conditions, etc. (see description(s) elsewhere herein), such activity or condition(s) having an impact on pressure. Exemplary pressure measurement affecting factor indicator(s) (PMAFI(s)) can include, e.g., “proxies” of such PMAF(s) (representation(s)/representative(s) of such activity(ies)/condition(s)). For example, rock volatile water can be a PMAFI of the PMAF water flooding; sulfate/SO can be a PMAFI of the PMAF salinity condition(s), etc. The method then comprises a step for considering whether or not factoring PMAF or PMAFI data, including, e.g., PMAFII data as applicable, into the model detectably or significantly improves upon the reliability of the model (e.g., provides an EQAM). If it is determined to do so, PMAF data, PMAFI data, PMAFII data, or combination(s) thereof are factored into the model, e.g., as PMAFIDQ. In certain respects, PMAFIDQ is characterizable as associated condition data (ACD) (ACD described elsewhere herein).

The method then considers whether or not the EERVCDQ, when factored with the PMAF/PMAFI/PMAFII data (PMAFIDQ), provides a highly reliable pressure measure/measurement (HRPM). If the EERVCDQ, when factored with PMAFIDQ, provides an HRPM, then the EERVCDQ/PMAF/PMAFI (EERVCDQ/PMAFIDQ) data set (DS) is used to evaluate GU pressure.

Alternatively, if the EERVCDQ, when factored with PMAFIDQ, do not provide an HRPM, the method comprises a step for considering if removal of some of the EERVCDQ data (EERVCD DQ), e.g., removal of select EERVCD DQ, from the model would result or results in an improved model. If removal of select EERVCD DQ is determined to result in an improved model, select EERVCD DQ is removed from the model.

Upon the select removal of EERVCD DQ, the method comprises a step to consider whether or not removing some of the EERVCD DQ from the model results in an EERVCD/PMAFIDQ data set (DS) with an HRPM. If the select removal of EERVCD DQ from the model results in an EERVCD/PMAFIDQ data set with a highly reliable pressure measure, then the EERVCD DQ/PMAFIDQ data set is used to evaluate geologic unit pressure.

Alternatively, if the selective removal of EERVCD DQ from the model does not result in an EERVCD/PMAFIDQ data set with a highly reliable pressure measure, then the method comprises a step for obtaining release-resistant rock volatile carbon dioxide quantity (RRRVCDQ).

Upon obtaining RRRVCDQ, the method comprises a step for considering whether or not RRRVCDQ provides an accurate measure of geologic unit pressure. If the release resistant rock volatile carbon dioxide quantity provides an accurate measure of geologic unit pressure, the RRRVCDQ is used to measure pressure. If release resistant rock volatile carbon dioxide quantity does not provide an accurate measure of geologic unit pressure, the sum of easily extractable rock volatile carbon dioxide and release resistant rock volatile carbon dioxide, e.g., the sum of easily extractable rock volatile carbon dioxide and release resistant rock volatile carbon dioxide data quantity(ies) can be evaluated to determine whether the sum provides an accurate measure of geologic unit pressure.

Also or alternatively, if RRRVCDQ does not provide an accurate measure of GU pressure, the sum of EERVCD and RRRVCD quantity(ies) does not provide an accurate measure of GU pressure, or neither the RRRVCDQ nor the sum of EERVCD and RRRVCD quantities provides an accurate measure of GU pressure, quantity(ies) of other pressure quantitative rock volatiles (PQRVs) are obtained as a step of the method.

Upon obtaining quantity(ies) of other PQRVs, the method comprises a step for considering whether or not the quantity of any other pressure quantitative rock volatile (PQRVQ) provides an accurate measure of geologic unit pressure. If the quantity of any other pressure quantitative rock volatile provides an accurate measure of GU pressure, the other PQRVQs are used to measure GU pressure. In aspects, such other PQRVQs are used alone to measure GU pressure (with or without PMAFIDQ); in other aspects, such other PQRVQs are used with one or both of EERVCD and RRRVCD quantity(ies) (with or without PMAFIDQ).

If the quantity of any other PQRVQ does not provide an accurate measure of GU pressure, the method concludes that rock volatile (RV) data should not be relied upon for the measurement of GU pressure.

In certain respects, this Example demonstrates an exemplary procedure for iterating QAM development until a highly reliable pressure measurement (HRPM) can be obtained therefrom by, e.g., step-by-step consideration of the addition or selective removal of data. In certain respects, this Example demonstrates that such QAM improvement can continue until no further selective data removal improves the model, no additional data capable of improving the model is available, or a combination thereof. Further, this Example demonstrates how QAM(s) can be developed from which pressure determination (measurement) in a geologic unit can be obtained using, at least in part, pressure quantitative rock volatile(s) (PQRV(s)), such as, e.g., rock volatile carbon dioxide, such as easily extractable rock volatile carbon dioxide (EERVCD) or, e.g., release resistant rock volatile carbon dioxide (RRRVCD), or other pressure quantitative rock volatile(s). This example further demonstrates how, e.g., difference(s) in data, e.g., differences in PQRVD, e.g., differences in easily extractable rock volatile carbon dioxide data, between samples collected from different wells having, e.g., different associated condition data (condition characteristics) such as, for example, salinity level(s) may impact model (QAM) development and how such model development may contemplate the select removal of data to improve upon its ability to provide highly reliable pressure measurements (HRPM).

Construction and Terms (Construction Principles and Description of Select Terms)

This section offers guidelines and resources intended to aid readers in understanding this disclosure.

General Terms and Principles

Persons that are the intended audience for this disclosure (“readers”) are persons having at least ordinary skill(s) in the practice of technologies discussed or used herein. Readers may also be called “skilled persons,” and such technologies and related publicly available prior knowledge are collectively referred to as “the art.” Terms such as “understood,” “known,” and “ordinary meaning” refer to the general knowledge of skilled persons.

The purpose of this document (“disclosure”) is to describe a number of new technologies (sometimes just called the “technology” and each discrete embodiment of which sometimes being called “a technology”) and to provide readers with the ability to practice such technologies (e.g., by exemplification, by description of elements thereof and relationship(s) of such elements to each other, and possibly other means). Readers will understand from this disclosure whether the technology/technologies include different forms, e.g., objects (such as systems, compositions, or devices), methods, or both, as will be clear from the disclosure. Disclosed technologies may be associated with surprising, unexpected, or otherwise inventive properties and, accordingly, in many cases, a described technology may reflect an invention.

The term “uncontradicted” means not contradicted by this disclosure, logic, or plausibility, the latter two elements being based on the knowledge of skilled persons.

Disclosed here are several different but related exemplary aspects (variations) of the technology (ies) (also referred to as, e.g., “cases,” “facets,” “respects,” or “embodiments”). The disclosure encompasses all such aspects as described individually and as can be arrived at by any combination of such individual aspects. Thus, uncontradicted, any reference to “aspects” (e.g., “according to aspects” or “in an aspect”) will be understood as referring to any of the other suitable aspects of the technology (ies) described herein. In this respect, the breadth and scope of the disclosure should not be limited by any exemplary aspect(s)/embodiment(s) herein. No language in this disclosure should be construed as indicating any element/step is essential to the practice of any technology or group of technologies provided by this disclosure unless such a requirement is explicitly stated. Uncontradicted, any aspect(s) described in any part of this disclosure can be combined with any other aspect(s) in any other part. Readers will discern that the term “aspect” is also sometimes used to refer to portions of this disclosure and, in such respects, is used in a manner similar to “subject” or “topic.”

Uncontradicted, all technical/scientific terms used here should be read, at least in one aspect, to have the same meanings as commonly understood by skilled persons, regardless of any narrower examples or descriptions provided here (including any term introduced initially in quotations). However, readers will also recognize that some aspects can be characterized by the inclusion of elements, steps, features, characteristics, etc., associated with specific descriptions provided here, and that such specific disclosures represent distinct embodiments of the disclosure apart from the corresponding aspect that is provided by interpreting the relevant aspect using any broader commonly used terminology or concept. Uncontradicted, disclosure of any aspect using known terms, which terms are narrowed by example or otherwise, implicitly discloses one or more related aspects in which the applicable terms are alternatively interpreted using the broadest reasonable interpretation of skilled persons.

Uncontradicted, the term “or” means “and/or” here, regardless of any inclusion of the actual phrase “and/or” (e.g., phrases such as “A, B, or C” and “A, B, and/or C” each simultaneously discloses aspects including (1) all of A, B, and C; (2) A and C; (3) A and B; (4) B and C; (5) only A; (6) only B; and (7) only C (and also support sub-groupings, such as “A or B,” “A or C,” etc.)). Uncontradicted, the use of a modifier/term such as “or both” in connection with elements (e.g., “element A, element B, or both”) does not mean or imply that elements listed only as “A or B” do not include combinations of A and B.

For conciseness, symbols are used where appropriate. E.g., “&” is used for “and,” & “˜” for “about.” Symbols such as < and > are given their ordinary meaning (e.g., “≤” means “less than or equal to” & “≥” means “greater than or equal to”). A slash “/” between terms here can represent “or” (“A/B” means “A or B”) or identify synonyms of an element, depending on context. The inclusion of “(s)” after an element or a step indicates that ≥1 of such an element is present, step performed, and the like. E.g., “element(s)” refers to both 1 element and ≥2 elements, with the understanding that each thereof is an independent aspect of the disclosure.

Uncontradicted, the term “also” means “also or alternatively.” Uncontradicted, the terms “here” & “herein” mean “in this disclosure.” The abbreviation “i.a.” (alternatively “ia” or “ia”) means “inter alia” or “(possibly) among other things.” “Also known as” is abbreviated “aka,” “AKA,” “a.k.a” (and can also or alternatively mean “is otherwise referred to here,” even if the relationship between the terms is not well known). The similar abbreviation “aka/ac” means “also known as or otherwise called” and is sometimes alternatively used to stress this point about the nature of the acronym. Uncontradicted, the term “elsewhere” means “elsewhere herein.”

Use of the abbreviation “etc.” (or “et cetera”) in association with a list of elements/steps means any or all suitable combinations of the recited elements/steps or any known equivalents of such recited elements/steps for achieving the function(s) of such elements/steps known in the art. Readers should interpret phrases like “and the like” similarly.

Uncontradicted, terms such as “and combinations,” “or combinations,” and “combinations thereof,” etc., regarding listed elements/steps, means any or all possible/suitable combinations of the associated elements/steps. Thus, e.g., uncontradicted, a phrase like “combination of any thereof” refers to any or all combinations.

Aspects may be described as suitable for method(s)/use(s) disclosed herein. Uncontradicted, terms such as “suitable” or “suitability” mean acceptable, appropriate, or, in aspects practical for performing a particular function/achieving particular state(s)/outcome(s), and typically mean effective, practical, and non-deleterious/harmful to associated valuable subject matter (human health, resource state, etc.). E.g., uncontradicted, the term “suitable” means appropriate, acceptable, or, in contexts sufficient, or providing at least generally or substantially all an intended function (of the element or overall whole of the aspect), without causing or imparting significant negative/detrimental impact. Uncontradicted, each method step, component/ingredient, or result element of the technical aspects of this disclosure should be understood to implicitly be mostly, generally only, substantially only, or only of an amount, degree, or character suitable in connection with its intended function, the intended function of the associated whole, or both. In some respects, suitability can be demonstrated through scientific studies and to a degree of significance through suitable tests/measures such as scientific tests, well-controlled and adequate studies (e.g., clinical studies), adequately powered trials, etc.

Steps, elements, compositions, devices, components, and the like also or alternatively can be characterized as being “effective.” Uncontradicted, any disclosed element is to be construed as being effective for its intended purpose and present in an effective amount, and any step performed is to be understood as being performed/applied effectively, such as in an effective amount or an effective number of times, etc. Uncontradicted efficacy can be judged by evaluating the element(s) ability to perform or contribute to the described function(s) or characteristic(s) associated with the component, device, step, etc., the overall aspect, or both in any manner disclosed here, known in the art, or both. For example, when applied to effects in organisms, such as people, efficacy and similar terms uncontradicted should be interpreted to at least implicitly disclose efficacy that can be measured (1) in a treated subject, (2) in a majority of subjects in a population, (3) in a statistically significant number of subjects in a population, (4) generally all subjects in a population, (5) substantially all subjects in a population, or (6) in a statistically significant number of or more of a typical or average subject of the class of subjects treated. Object elements or steps are, uncontradicted, understood to be implicitly present in “effective amount,” and, uncontradicted, any described class of object or step in connection with a device, system, composition, or method, is understood to be present in the associated whole or performed in association with the associated entire method in an effective amount, effective way, or having effective characteristic(s), which generally means, an amount that the described object/component or step is effective for the described function(s) associated with the element, associated whole, or both. A “step” is not necessarily a general “step for” performing a function.

Uncontradicted, heading(s) (e.g., “Construction and Terms”) and subheadings used here are included for convenience and do not limit the scope of any aspect(s). Uncontradicted, aspect(s), step(s), or element(s) described under one heading can apply to other aspect(s) or step(s)/element(s) here.

Ranges of values here represent each value falling within a range within an order of magnitude of the smallest endpoint of the range, without having to write each value of the range explicitly. E.g., a recited range of 1-2 implicitly discloses each of 1.0, 1.1, 1.2, . . . 1.9, and 2.0, and 10-100 implicitly discloses each of 10, 11, 12, . . . 98, 99, and 100. Uncontradicted, all ranges include the range's endpoints, regardless of how a range is described. E.g., “between 1-5” includes 1 and 5 in addition to 2, 3, and 4 (and all numbers between such numbers within an order of magnitude of such endpoints, e.g., 1.0, 1.1, . . . 4.9, and 5.0). For the avoidance of doubt, any number within a range, regardless of the order of magnitude of the number, is covered by the range (e.g., a range of 2-20 covers 18.593). Uncontradicted, readers will understand that any two values in a range provided as a list herein can be combined as endpoints to form a range defining a more particular aspect of the disclosure (e.g., if a list of values 1, 2, 3, 4, and 5 of element X is provided, readers will understand that the disclosure implicitly discloses an aspect comprising 2-4 X, 3-5 X, and 1-3 X, etc.

Terms of approximation (e.g., “about,” “˜,” or “approximately”) can be used here (1) to refer to a set of related values or (2) where a precise value is difficult to define (e.g., due to limits of measurement). Uncontradicted, all exact values provided here simultaneously/implicitly disclose corresponding approximate values and vice versa (e.g., disclosure of “about 10” provides explicit support for the use of 10 exactly in such aspect/description). Ranges described with approximate value(s) include all values encompassed by each approximate endpoint, regardless of presentation (e.g., “about 10-20” has the same meaning as “about 10-about 20”). The scope of value(s) encompassed by an approximate term typically depends on the context of the disclosure, criticality or operability, statistical significance, understanding of the art, etc. In the absence of guidance here or in the art for an element, terms such as “about” when used in connection with an element should be interpreted as ±10% of the indicated value(s) and implicitly disclosing ±5%, ±2%, ±1%, and ±0.5%. Two or more values may be characterized as approximately similar if they are considered to be about the same on such bases.

Aspects may be associated with the description of a change or difference. In some cases, similarity or difference can be assessed as similar or not (i.e., statistically similar or different). In cases, a difference or change means a “sizable” change or difference, which means a change or difference that is beyond what would be considered substantially the same or approximately the same (approximately or about in such contexts being either defined in the art or +/−10% or being recognized by readers as not having different characteristics or outcomes that are substantially different with respect to intended function) (e.g., a change of ≥12.5%, ≥15%, ≥20%, etc., such as 12.5%-50%, 12.5%-33%, 15-45%, etc., or 15-150%, 20-200%, 30-300%, etc.). In aspects, a change or difference in element(s) can be characterized as a “major” change or difference, which means a change or difference that is an increase or decrease (1) of at least 33% and can be a change of at least 50%, at least 75%, at least 100%, at least 150%, at least 200% (2×), e.g., at least 0.5×-5×, 10×, or 20× or (2) by one or more (e.g., 2 or 3) order(s) of magnitude. Uncontradicted, elements, compositions, outcomes, etc., described as different herein or described as different in any of these ways provide implicit support for corresponding aspects in which the applicable change/difference is characterized as one of the other differences. The modifier “constrained” means that a value, such as a sizable value, a detectable value, or a major value, is limited to 50% or less of a whole (e.g., 45% or less, 40% or less, 37.5% or less, or 35% or less of a whole). Uncontradicted, any disclosure of such a term herein provides implicit support for an otherwise corresponding aspect in which the scope of the applicable value can be characterized as a constrained value.

This disclosure includes aspects of the technology that are associated with particular characteristics, such as amounts of components (or ranges thereof). In cases, several such characteristics of varying scope may be provided. Readers will understand that each such characteristic can be associated with particular properties that distinguish such aspects from other aspects, and, accordingly, each such range can be viewed as critical to a particular aspect of the technology, even if the associated results, properties, functions, etc., associated with such aspects are not directly/explicitly communicated in association with any such characteristics.

Lists of aspects, elements, steps, and features are sometimes employed for conciseness. Unless indicated, each member of each list should be viewed as an independent aspect. Each aspect defined by any individual member of a list can have and often will have non-obvious properties vis-à-vis aspects characterized by other members of the list.

Uncontradicted, the terms “a” and “an” and “the” and similar referents encompass both the singular and the plural form of the referenced element, step, or aspect. Uncontradicted, terms in the singular implicitly convey the plural and vice versa herein (in other words, disclosure of an element/step implicitly discloses the corresponding use of such/similar elements/steps and vice versa). Hence, e.g., a passage regarding an aspect including X step supports a corresponding aspect including several X steps. Uncontradicted, any mixed use of a referent, such as “a” in respect of one element/step or characteristic and “one or more of” concerning another element/step or characteristic in a paragraph, sentence, aspect, or claim, does not change the meaning of such referents. Thus, for example, if a paragraph describes a composition comprising “an X” and “one or more Ys,” the paragraph should be understood as providing disclosure of “one or more Xs” and “one or more Ys.”

“Significant” and “significantly” mean results/characteristics that are statistically significant using ≥1 appropriate test(s)/trial(s) in the given context (e.g., p≤0.05/0.01). “Detectable” means measurably present/different using known detection tools/techniques. The acronym “DOS” (or “DoS”) means “detectable(ly) or significant(ly).” The term “measurably” means at a measurable level and, uncontradicted, comprises at a suitable measurable level/amount. The term detectable provides implicit disclosure for aspects that are “measurable,” and the term “measurable” implicitly supports aspects where the measured or measurable element is “detectable.” Uncontradicted, any aspect including an element described as “similar” to another element implicitly discloses, at least as one aspect, where the similarity comprises statistical similarity. Uncontradicted, any reference to a comparison, change, or other relationship between elements (e.g., a result) characterized by similarity or detectability also implicitly discloses changes or comparisons where the difference is approximately/about the same (e.g., within +/−10% of each other).

Uncontradicted, any value provided here that is not accompanied by a unit of measurement (e.g., a weight of 50 or a length of 20), either any previously provided unit for the same element/step or the same type of element/step will apply, or, in cases where no such disclosure exists, the unit most used in association with such an element/step in the art applies.

Uncontradicted, the terms “including,” “containing,” “comprising,” and “having” mean “including, but not limited to,” or “including, without limitation.” Uncontradicted, use of terms such as comprising and including regarding elements/steps means including any detectable number or amount of an element or including any detectable performance of a step/number of steps (with or without other elements/steps). Uncontradicted, “a” means one or more, even when terms such as “one or more” or “at least one” are used in association with the referent “a.”

For conciseness, description of an aspect “comprising” or “including” an element, concerning a collection/whole (e.g., a system, device, or composition), implicitly provides support for any detectable amount/number, such as, e.g., between a detectable or measurable amount and about 33%, such as, e.g., ≥˜1%, ≥˜5%, ≥˜10%, ≥˜20%, ≥˜25%, or ≥˜33%, as in, for example, ˜0.00001%-˜33%, ˜1%-˜33%, ˜5%-˜33%, ˜10%-˜33%, ˜15%-˜33%, ˜20%-˜33%, ˜25%-˜33%, or, e.g., ˜30%-˜33%.

In certain respects, description of an aspect “comprising” or “including” an element concerning a collection/whole implicitly provides support for amounts greater than about 33%, such as, e.g., ˜50%-˜75%, such as, e.g., ≥˜50%, ≥˜51%, ≥˜51%, ≥˜66%, or ≥˜70%, such as, e.g., ˜55%-˜75%, ˜60%-˜75%, or ˜70%-˜75%, such as, e.g., ˜35%, ˜40%, or ˜45%.

In still further respects, description of an aspect “comprising” or “including” an element concerning a collection/whole implicitly provides support for amounts greater than about 80%, such as, e.g., ˜80%, ˜85%, ≥˜90%, ˜93%, ≥˜95%, ≥˜99%, or ˜100% of the whole/collection being made up of the element, as in, e.g., ˜77% or more, or essentially all of the whole/collection being made up of the element (i.e., that the collection consists essentially of the referenced element). Similarly, a method described as including a step concerning an effect/outcome implicitly provides support for the referenced step, providing ≥˜1%, ≥˜5%, ≥˜10%, ≥˜20%, ≥˜25%, ≥˜33%, ≥˜50%, ≥˜51%, ≥˜66%, ≥˜75%, ≥˜90%, ≥˜95%, ≥˜99%, or ˜100% of the effect/outcome, representing ≥˜1%, ≥˜5%, ≥˜10%, ≥˜20%, ≥˜25%, ≥˜33%, ≥˜50%, ≥˜51%, ≥˜66%, ≥˜75%, ≥˜90%, ≥˜95%, ≥˜99%, or ˜100% of the steps/effort performed, or both. Explicit listing of percentages of elements in connection with particular aspects does not limit or contradict such implicit disclosure. Uncontradicted, readers should interpret terms such as “essentially all” or “essentially” consistent with the concept of “consisting essentially of.”

Uncontradicted, terms such as “comprising” when used in connection with a step of a method provide implicit support for performing the step once, ≥2 times, or until an associated function/effect is achieved.

Further, readers will understand that, uncontradicted, use of terms such as “comprising” or “including” provides aspects for which the referenced element (or step(s), etc.) is “generally” present, “substantially” present, “essentially” present, or is present. In certain alternative aspects, “comprising” or “including” can refer to something that is mostly present, about equally present, or is present in another amount, such as about 40%, about 50%, etc. In certain aspects, accordingly, the use of “comprising” and “including” provides support for referenced element(s) to be present in “significant” amounts (e.g., a statistically significant amount) or in DOS amounts, in “some” amount, or, e.g., a “predominant” amount.

Uncontradicted, any disclosure of an object or method (e.g., composition, device, or system) “comprising” or “including” element(s) provides implicit support for an alternative corresponding aspect that is characterized by the object consisting of that element or “consisting essentially of” that element (excluding anything that would “materially affect” the “basic and novel characteristic(s)” of any inventive aspect of this disclosure). Uncontradicted, any specific use of phrases such as “consists of” and “consists essentially of” herein does not modify this construction principle.

Readers will understand that the disclosure of concentrations/amounts/numbers of different elements/components acts as a disclosure of compositions/devices characterized by relationships in such amounts formed between them. Accordingly, uncontradicted, any disclosure of amounts/concentrations/numbers that reflects a suitable relationship between elements/components provides an implicit disclosure of a composition/device that varies from the specifically disclosed amounts/concentrations/numbers, but which retains the relationship. For example, if the disclosure provides 1 unit of A and 3 units of B, readers will understand that this means that the disclosure provides a corresponding aspect characterized by the inclusion of suitable amounts of A and B, wherein such amounts are present in a ratio of about 1 part A to about 3 parts B.

Readers will understand the “basic and novel characteristic(s)” of an invention provided in this disclosure, and the scope of what constitutes a “material effect” (or “material effect”) of such “basic and novel characteristics” will vary with the specific applicable aspect at issue. Uncontradicted, the basic and novel characteristics of any inventive aspect include the specific recited and associated elements of an aspect and exclude any other element that significantly detracts from the intended function(s) of the recited elements, that introduce significant new functions that are unrelated to the intended function(s), that significantly reduce the performance of the function(s), or that significantly negatively change other characteristics of performing such function(s) (e.g., by increasing the cost of performing the functions in energy, money, or both). Uncontradicted, the basic and novel characteristics also include at least significantly retaining the suitability, effectiveness, or both, of recited elements or the overall aspect. Accordingly, a material effect can be an effect that reduces, diminishes, eliminates, counteracts, cancels, or prevents one or more of such functions in one or more respects (e.g., delaying onset, reducing scope, reducing duration, reducing output, reducing the level of applicability, reducing effect, or combinations thereof). In an aspect, a material effect is one that changes such functions by making such functions impractical, difficult to obtain, or materially more expensive or otherwise costly in terms of inputs. From this and the other guidance provided herein, readers can understand the scope of an aspect that is defined by/as consisting essentially of a collection of elements.

Uncontradicted, the term “one” means a single type, single iteration/copy/thing, of a recited element or step, or both, which will be clear from the context of the relevant disclosure. For example, the referent “one” used with respect to a component of a composition/article or system can refer to one type of element (which may be present in numerous copies, as in the case of an ingredient in a composition), one unit of the element, or both. Similarly, “one” component, a “single” component, or the “only component” of a system typically means 1 type of element (which may be present in numerous copies), 1 instance/unit of the element, or both. Further, “one” step of a method typically means performing one type of action (step), one iteration of a step, or both. Uncontradicted, a disclosure of “one” element provides support for both, but uncontradicted, any claim to any “one” element means one type of such an element (e.g., a type of component of a composition/system/article).

Uncontradicted, the term “some” means ≥2 copies/instances or ≥5% (e.g., ≥7.5%, ≥12.5%, ≥17.5%, >27.5%, or ≥37.5%) of a listed collection/whole is or is made up of an element. Regarding methods, some means ≥5% of an effect, effort, or both is made up of or is attributable to a step (e.g., as in “some of the method is performed by step Y”) or indicates a step is performed ≥2 times (e.g., as in “step X is repeated some number of times”). Terms such as “considerable amount” or “considerable portion” mean at least 1%, 2%, or 2.5%, but less than most of a whole, such as 2.5-25%, e.g., 5-25%, 5-20%, 7.5-22.5%, 10-20%, 2.5-10%, 2.5-12.5%, 5-15%, etc. Terms such as “sizable portion” mean 10-50% and in aspects 15-50%, 20-50%, or 25-50%, or subranges thereof (e.g., 15-45%, 20-40%, 25-45%, etc.). Terms such as “predominately,” “most,” or “mostly” (and “primarily” when not used to refer to an order of events or “mainly”) means detectably >50% (e.g., mostly comprises, predominately includes, etc., mean >50%) (e.g., a system that mostly includes element X is composed of >50% of element X). The term “generally” means ≥75% (e.g., generally consists of, generally associated with, generally comprises, etc., means ≥75%) (e.g., a method that generally consists of step X means that 75% of the effort or effect of the method is attributable to step X). “Substantially” or “nearly” means ≥95% (e.g., nearly all, substantially consists of, etc., mean ≥95%) (e.g., a collection that nearly entirely is made up of element X means that at least 95% of the elements in the collection are element X). Terms such as “generally free” of an element or “generally lacking” an element mean comprising ≤25˜% of an element, and terms such as “substantially free” of an element mean comprising ≤˜5% of an element. Uncontradicted, any aspect described as “generally comprising” or “generally consisting” of an element implicitly discloses an element that “substantially comprises” the element. The same principle applies to any disclosure where an aspect is described as being “generally free” of an element.

Uncontradicted, phrases such as “substantially identical” or “substantially similar” may be used to refer to element(s)/component(s)/ingredient(s)/thing(s) (e.g., composition, system, device, etc.) or step(s)/method(s) that have the same or about the same characteristic(s) or achieve the same or about the same result(s), typically in a similar way, as a referenced element/thing or step/method or otherwise do not meaningfully differ in intended result and manner of achieving such a result or are otherwise recognized in the art as not differing or not differing substantially in the relevant context (e.g., by being considered equivalents). Uncontradicted, readers will understand that a “substantially identical” or “substantially similar” element/thing or step/method when compared to a comparator thing/element or method/step means that the referenced element/thing or step/method exhibits such a similar function as a comparator at identical, approximately identical, or statistically similar amounts as the comparator thing or method when applied under similar conditions of use. Again, where statistical, approximate, or other measured comparisons are not possible, readers will understand the phrase as encompassing those things known as being identical or substantially identical to the referenced element/step or are described as such herein.

Uncontradicted, any aspect described concerning an optionally present element(s)/step(s) also provides implicit support for corresponding aspect(s) in which one, some, most, generally all, nearly all, essentially all, or all such element(s) are lacking/step(s) not performed, in respect of the relevant aspect. E.g., disclosure of a system comprising element X implicitly also supports a system lacking element X. That is, readers will understand that any element, feature, step, or characteristic of any aspect of the technology recited herein as being present in an aspect also implicitly provides support for the element, feature, step, or characteristic as being excluded from a corresponding/similar aspect implicitly disclosed by the explicit positive disclosure and vice versa. Uncontradicted, changes to tense or presentation of terms (e.g., using “comprises predominantly” in place of “predominately comprises”) do not change the meaning of the corresponding term/phrase.

Uncontradicted, all methods provided here can be performed in any suitable order regardless of presentation (e.g., a method comprising steps A, B, and C can be performed in the order C, B, and A; B and A and C simultaneously, etc.). Uncontradicted, elements of a composition/device/system can be assembled in any suitable manner by any suitable method. In general, any methods and materials similar or equivalent to those described here can be used in the practice of embodiments in at least the broadest version of the relevant aspect. Uncontradicted, the use of ordinal numbers such as “first,” “second,” “third,” and so on is primarily, though not exclusively, intended to distinguish respective elements rather than to limit the disclosure to a particular order of those elements, importance, or configuration.

Any elements associated with a function can be alternatively described as “means for” performing a function in a composition/device/system or a “step for” performing a part of a method, and parts of this disclosure refer to “equivalents,” which means known equivalents known in the art for achieving a referenced function associated with disclosed mean(s)/step(s). However, no element of this disclosure or claim should be interpreted as limited to a “means-plus-function” or “step-plus-function” construction unless such intent is clearly indicated by the use of the terms “means for” or “step for.” Terms such as “configured to” or “adapted to” do not indicate “means-plus-function” interpretation but, rather, describe element(s)/step(s) configured to, designed to, selected to, or adapted to achieve a certain performance, characteristic, property, or the like using teachings provided here or in the art.

As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary,” “representative,” or “illustrative,” etc., should not necessarily be construed as preferred or advantageous over other embodiments. Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

Except where explicitly indicated or clearly indicated by context, terms such as “improved” or “better” mean DOS increased (e.g., sizably, constrained sizably, majorly, or constrained sizably, increased, etc.). In some respects, as will be clear from context or knowledge, terms such as “improved” or “better” mean DOS “reduced,” such as concerning reducing negative elements of a method or composition or performance characteristic (as in, e.g., with a device/system). Uncontradicted, terms such as “enhanced,” “improved,” “better,” and the like are used synonymously.

Any reference to a trademark name (product trademark ™) incorporates all publicly known information about that product as of the filing date of this Application. Further, reference to a trademarked name is intended to refer to those product(s) as recognizable to readers as of the date of filing of this disclosure that are recognized as equivalents to such product(s), such as product(s) that perform the same function/are recognized as operationally the same (operate on the same key principles or by way of the same feature(s)); comprise the same key characteristic(s), component(s), mechanism(s), etc. Readers should recognize, however, that simplified and alternative product(s) or element(s) from those provided herein could be incorporated, used, or provided, e.g., in aspects, modification(s) of the product(s) specifically described herein which maintain the spirit of this disclosure are incorporated or can be considered as “means” type equivalents of a referenced product/element.

Regarding figures, graphs, and the like disclosed in this Application, such figures, graphs, and the like are exemplary, and their disclosure concurrently provides disclosure of anything statistically significantly close to or which is “about” the same as the disclosure—e.g., as the data, feature(s)/characteristic(s), and demonstrated performance characteristic(s) provided. For example, any/all data points provided in, e.g., graph(s) disclosed in this Application incorporate data point(s) within a range of statistical similarity and points which are about the same (e.g., within a reasonable fraction of an order of magnitude) as those exemplified. Graph(s) or figure(s) provided herein should be understood to include, where such feature(s) are present, data providing similar patterns, shapes, curves, peaks, etc.

All references (e.g., publications, patent applications, and patents) cited herein are hereby incorporated by reference as if each reference were individually and specifically indicated to be incorporated by reference and set forth in its entirety herein. Uncontradicted, any suitable principles, methods, or elements of such references (collectively “teachings”) can be combined with or adapted to aspects. However, citation/incorporation of patent documents is limited to the technical disclosure thereof and does not reflect any view regarding the validity, patentability, etc., thereof. Uncontradicted, in the event of any conflict between this disclosure and the teachings of such documents, the content of this disclosure takes precedence regarding interpreting aspects of the disclosure. Numerous references are cited here to concisely incorporate known information and aid skilled persons in putting aspects into practice. While efforts have been made to include the most relevant references for such purposes, readers will understand that not every aspect of every cited reference will apply to every aspect of this disclosure or the technology.

While elements disclosed in such incorporated references can be combined with aspects of the disclosure provided herein, readers will understand that the described technology is intended to stand apart from such disclosures and, accordingly, uncontradicted, in aspects, any element(s) of the objects or methods of any such references can be considered to be excluded from the scope of what is presented as new technology here (e.g., if reference A discloses object or element B, any aspect that is not directed to object or element B can be characterized by, as one aspect, the lack of object or element B).

All original claims contained in this disclosure, when filed, are incorporated into this specification as if they were a part of the description.

TERMS AND PRINCIPLES RELATING TO THE TECHNICAL FIELD

Readers should note that the Summary/Detailed Description sections or other sections may provide some terms specific to the field, also or alternatively, to the terms described in this section. Uncontradicted, any repetition of a term description is meant to reflect alternative aspects that are characterized by the different meanings or examples of such terms.

The term “geologic resources” is described in the Background. Geologic resources can include or be associated with geologic material(s). The “state” of geologic resources is also described in the Background. In general, disclosure of the term “state” in connection with methods of this disclosure (“methods”) is to be understood as implicitly providing a corresponding aspect aimed at determining the characteristic(s) of resource(s).

The term “geologic material” is sometimes used to mean a composition composed of (e.g., substantially composed of/generally composed of) or comprising material in, obtained from, or derived from a geologic region (e.g., an area/zone). Examples of such materials can include drill cuttings, core samples, muds, etc. In aspects, the material comprises a solid material (e.g., a material that is mostly, if not entirely or essentially classifiable as rock). In aspects, the material is mostly, generally only, substantially only, or only composed of solid geologic material(s) (e.g., rock(s)). The term “rock material(s)” generally means any suitably analyzable material(s) that are (e.g., generally are) or that comprise solid rock materials (e.g., drill cuttings, such as PDC cuttings).

The term “geologic unit” is generally understood as referring to a discrete geologic area or space/zone, e.g., basin or other structural province, group, formation, member, petroleum system, play, area, site, environment/location, or position from which geologic materials may be obtained e.g., a specific well, borehole, or borehole environment (or a type of such GUs). Many of these terms (e.g., basin and structural province (aka, a “province”)) are well understood in the art and, accordingly, are not described in significant detail here. Uncontradicted, readers will understand that any disclosure directed to a GU or a particular type of a GU of appreciable size (e.g., a system/basin, a region/area/play, a field, a well/borehole environment, or a zone) provides implicit support for a corresponding aspect in which the disclosed term is substituted with portion(s) of the GU, or a single portion of the GU.

A “system” such as a “petroleum system” is understood to mean a relatively large area characterized by common geologic features (a basin (a petroleum basin) or a province (a petroleum province)) that comprises petroleum and typically includes the elements that provide for commercially useful petroleum reservoir(s) (e.g., suitable s rock, migration path, reservoir rock, seal, and trap features). A petroleum system, in particular, might include other defined regions such as plays, or smaller areas/regions (e.g., fields, or even smaller geographic locales such as sites—e.g., wells or prospects).

The term “region” or “area” can be used to describe geographic measures that are smaller than a system (e.g., a play or a field). A region (or area) can be defined by a group of resources or structures used to utilize/access resources, such as oil production/exploration fields (each typically comprising many wells/sites). An area/region can also refer to a play or a subpart of a play. As demonstrated in this paragraph, the terms “area” and “region” in such contexts may be used interchangeably herein.

The term “play” has various meanings in the art. In a broad sense, a “play” means a geographically delineated area where producible petroleum or similar resource has been proven or, sometimes, where sufficient and suitable geologic factors are present so that producible petroleum or other resource can be proven (the latter type is also called a “prospect”). A “play” commonly refers to a region defined by, or at least including, a group of oil/hydrocarbon resource “fields” (each “field” typically comprising many wells/sites or prospect sites), which often share the same set of geological circumstances (e.g., formations present or other shared/overlapping structural/stratigraphic features). Oklahoma, USA, for example, has many plays, but two notable plays making headlines across the nation are the “SCOOP” (South Central Oklahoma Oil Province) and the “STACK” (Sooner Trend Anadarko Basin Canadian and Kingfisher Counties). The petroleum-rich STACK play is characterized by the presence of the Oswego, Meramec, Osage, and Woodford formations. Terms like play and region can be used to refer to geographic areas including two or more wells, prospects, or both.

“Geologic resources” (“GRs”) are described in the Background. Geologic resources can include or be geologic reserves. Uncontradicted, any use of the term geologic resource herein implicitly discloses a corresponding aspect in which the GR can be characterized as a reserve and vice versa. Reservoirs/compartments are examples of GRs.

A “site” is typically a geographic area defined by and closely associated with ongoing, prior, or prospective activity with respect to geologic resource(s), such as a petroleum well, e.g., a petroleum well or an area of prospective petroleum drilling (prospect) within a region (e.g., a petroleum province), or, for example, a location where CCS/CCUS activities are being or may be carried out.

Readers will recognize that these terms often differ in ways that are not completely defined in the art and reflected in the guidance provided here. In other words, there can be overlap or the same meaning in different terms for geologic units—e.g., system, region, play, and field. Differences in such terminology are often not clear or arbitrary, or differently defined. In general, a system or region is larger than a play, which is larger than a field, and a field contains many sites (working/active, prospective, or dormant but potentially revivable). A field might be associated with, e.g., a particular reservoir, whereas a play can be associated with several reservoirs, and a system can include several plays

Readers will further recognize that many of the types/categories of GUs/GRs described above (e.g., basin/province, play, field, and site) are characterized/defined only in geographic terms (i.e., by their area/location with respect to the surface). Uncontradicted, and typically, any suitable portion of the subterranean environment below such a geographically defined GU can be considered “within” the applicable GU (e.g., a space that is defined by a formation or a reservoir can be within a field).

Where a GU/GU type of interest is defined by making up or falling within a portion of a subterranean environment, such a GU/GU type may be described/defined based on dimensional (3d/3D) coordinates, relevant measurements, or simply depth measurements. E.g., unique 3d coordinates/relevant measures (e.g., relative to the earth's surface or a defined position, such as a borehole or borehole environment), which allow such location to be spatially defined in a three-dimensional map.

A depth measure can be the only term used to define locations of subterranean features, structures, resources, etc. (e.g., the location of a formation, reservoir, etc.). Often x and y coordinates or measures are not provided with such locational information/characterizations because the relevant GU/resource/feature has or is within a space defined by a fixed/substantially fixed/uniform and relatively small horizontal space (e.g., a vertical well/borehole), such that the depth of a geologic resource (GR) or feature/structure is considered the only relevant location factor. In other cases, the GU/GR is locationally defined only by depth because it is assumed or known to occupy a relatively uniform or stable depth zone/space across a larger GU (e.g., a formation can be known to occupy approximately the same depth within a field, play, or basin).

The terms “well” and “borehole” can sometimes be used in common parlance to distinguish mechanisms of drilling (e.g., a borehole typically drilled by machine and being small in diameter, a well typically being sunk by hand and being relatively larger in diameter). Uncontradicted, the two terms are used interchangeably here and, uncontradicted, refer in at least one aspect to a vertical or horizontal shaft used for discovery, characterization, storage, and/or extraction/production of geologic resources.

A well or borehole may be currently used or previously have been used as, e.g., a petroleum exploration/production well, a carbon capture storage well, a geothermal well, or a well for hydrogen or helium production.

In aspects, a relevant geologic unit can comprise one or more formations or, more typically, a portion of one or more formations. A “formation” is understood in the art. In one sense, the term “formation” is used to indicate an identified zone of strata (or approximately/roughly identified zone of strata) having similar lithology. In some cases, a formation also or alternatively may be defined by other characteristics, such as biostratigraphic characteristics, chemostratigraphic characteristics, or both, and sometimes such characterizations of a formation are used interchangeably. Typically, a formation is understood to comprise a series of strata/beds that are distinct from other beds above and below and are thick enough to be shown on geologic maps. Formations dominated by a rock typically include the dominant rock in the formation's name (e.g., the “Woodford Shale Formation” found in several parts of Oklahoma). However, formations in some cases can contain a variety of related or interlayered rock types, such as the Summerville Formation of Utah, which consists of thin alternating beds of shale, siltstone, and sandstone. Formations can be divided into sub-formations or “members” based on such or other known characteristics. Sometimes in the art and in this disclosure, aspects of methods are described as applying across different “formations.” Readers will understand that such use of the term “formation” is meant to refer to material of a defined formation in a site/area which, as exemplified above, may comprise one or more rock types or other mixture(s) of geologic material(s).

The term “tight” as used in terms such as “tight rock formation,” “tight formation,” “tight spaces,” “tight rock,” or “tight area,” etc., means an area/section of geologic material that is mostly, generally, substantially, or entirely is characterized by a permeability of 10 millidarcies (“mD” or “md”) or, for example, about 10 mD or less, e.g., ˜7, ˜5, ˜4, or ˜3 mD or less (e.g., ˜10⁻⁸-˜10 mD, such as ˜10⁻⁶-˜10 mD, ˜10⁻⁵-˜10 mD, ˜10⁻²-˜10 mD, ˜0.1-˜10 mD, ˜10⁻⁶-˜5 mD, ˜10⁻³-˜5 mD, or ˜0.1-˜5 mD. In aspects, tight formations/areas are characterized by a narrower range of permeability (e.g., ˜10⁻⁸-˜3 mD, ˜10⁻⁵-˜3 mD, ˜10⁻³-˜3 mD, ˜0.1-˜3 mD, ˜10⁻⁸-˜1 mD, ˜10⁻⁵-˜1 mD, ˜10⁻³-˜1 mD, or ˜0.05-˜3 mD). In petroleum production, tight rocks may require fracking or require enhanced oil recovery (EOR) operations, such as water flooding or CO₂ injection, to economically extract associated petroleum or other local resources.

Terms such as “zone” here and in some uses in the art refer to a three-dimensional (3d/3D) subterranean “space” (sometimes, aka, a “volume”), e.g., of a GU or GR that is sufficiently defined by coordinate(s)/relevant measure(s) (e.g., depth measures or x, y, and z coordinates) that allow the zone to be located or approximately/relatively located in a map of the relevant subterranean environment (the term “pay zone” for example, usually refers to the depth at which a petroleum reserve can be commercially accessed and exploited by a well or closely associated wells in a field). A zone may characteristically be described/located with respect to the presence of a resource (an energy-producing hydrocarbon reserve), a structure (e.g., a fault), or an activity (e.g., a space of a GU that is utilized for CCS). A zone can also refer to a space that is defined by the space comprising all specific locations from which samples were taken (e.g., positions of a borehole or positions from different wells within a field). Unless arbitrarily defined, a zone is typically defined by shared characteristics. E.g., a gas or petroleum pay zone is typically defined by similar or approximately the same porosity and permeability characteristics, and usually will have similar depth/formation, hydrocarbon pressure, and saturation characteristics. In methods, a zone typically includes 100s, if not 1000s, of analyzed specific locations (ASLs, aka SLs). Such specific locations, as described below, are typically considered to be small (e.g., about 1-100 ft., such as about 2-80 ft., about 3-60 ft., or 5-50 ft., about 2-40 ft., about 3-30 ft., about 1-25 ft., about 1-15 ft., about 1-10 ft., or about 1-5 ft.). In contrast, a zone in a method can be much larger (e.g., having a depth of about 2-500 ft., 10-500 ft., 15-350 ft., 20-400 ft., 2-400 ft., 3-300 ft., 5-150 ft., 1-100 ft., 2-200 ft., 1-50 ft., or 3-30 ft. and having an area that covers a few miles up to several thousand miles. Systems (e.g., basins) are larger than zones having areas on the order of 5,000 to 100,000 square miles or more.

A “reservoir” is understood in the art to refer to an area/environment with sufficient porosity and permeability to store and transmit fluids, such as carbon dioxide, hydrocarbon gases, fluids such as petroleum/oil, or, in aspects, helium, or hydrogen, and, in aspects, liquids, such as crude oil. The term “compartment” is understood to refer to a zone/space that is effectively sealed off from another, with little to no fluid communication occurring between two or more compartments. The phrase “reservoir/compartment” can be used to reflect that these terms are, uncontradicted, used interchangeably here.

Uncontradicted, terms such as “zones” and “reservoirs” can overlap or be synonymous in cases. Sometimes the term “leg” is informally used in the art in a manner similar to the term zone (e.g., a “water leg”).

Terms like “specific location” (“SL”) (sometimes simply called a “location” and sometimes combined with the term geologic unit or its acronym (e.g., “GUL” or “GUSL”)) can be used here to refer to a discrete place/space in a larger subterranean geologic unit that is characterizable by unique 3d coordinates/relevant measures (e.g., relative to the earth's surface or a defined position (e.g., in a borehole or borehole environment)), or an area about such a point that is expected to share common characteristics, and which, in either case, allow such location to spatially defined in a in three-dimensional map. Like the description of other 3-dimensional spaces in a geologic unit, a point is often described simply by referring to depth, and the term depth is often used in place of point when the context dictates that x and y coordinates are not highly variable or relevant, etc. (e.g., drill cuttings may be described as obtained from depth 1, depth 2, or depth 3 of a well). A point typically will mean a space defined in x and y coordinates by an area of or about the borehole and a specific depth or range of depth. Uncontradicted, any disclosure of aspects referring to defined points implicitly disclose aspects where one, some, or all of the dimensional sizes of the “point” or “specific location” is characterized as meaning the defined point/SL and a suitable space around the point/SL, which can be defined by a relative or fixed standard, by substantially identical or identical characteristics as the point, or both. In aspects, an SL's position/location may be expressed in any suitable manner, e.g., as latitude and longitude for the x and y coordinates and depth (e.g., relative to the surface or a defined feature, resource, structure, etc.), or by a z coordinate of a larger. A specific location may have any suitable size about a defined point/coordinate/location. Exemplary specific location sizes include, e.g., +/−1 ft, about +/−2 ft, about +/−5 ft, about +/−10 ft, about +/−20 ft, +/−35 ft, about +/−50 ft, ˜+/−75 ft, about +/−100 ft, or ˜+/−150 ft, +/−200, 250, 300, or ˜+/−500 ft. In aspects, most, generally all, or all specific locations of a method have the same 3-dimensional size, have a statistically similar size, or have approximately the same size.

Given the common use of terms like “location,” “point,” “zone,” “space,” and “region,” and their susceptibility to multiple meanings, readers should examine the context of the term and determine if it is meant this way or as referring to some other type of position/volume/area. Uncontradicted, such terms refer, in at least one aspect/interpretation, to the connotations provided in this section.

Uncontradicted, the “pressure” measured by methods is any suitable type of pressure, but typically is mostly, generally, or entirely fluid pressure as measured at the various specific locations/GUSLs (e.g., pressure that might ordinarily be characterized as hydrostatic pressure) or pressure throughout a GU (e.g., in a formation/zone in a well, field, or play) or portion of a GU (e.g., in a portion of a play, field, well, etc.). In aspects, some, most, generally all, substantially all, or all of the relevant fluid pressure is attributable to GU liquids. In aspects, some, e.g., a constrained sizable or constrained major amount of the GU pressure, is gas pressure.

Uncontradicted, terms such as “direct pressure” are used to refer to quantifications of geologic unit liquid pressure that (1) are made by a method other than a method relying on PQRVQD (e.g., a method relying on PQRVQD-derived data)

Uncontradicted, terms such as “data” and “information” herein are intended to implicitly disclose any and all forms of applicable or possibly applicable data, information, etc., of any suitable and applicable kind, form, etc. Readers will understand that the term “data” can be used to refer to a particular item of information (fact, measure, record, etc.) or a collection of such items and, uncontradicted, should be interpreted as implicitly meaning both. In aspects, the term “data set” is used to specifically refer to a collection of data items/records. Uncontradicted, the term data set can be interchanged with data or information, etc. In many aspects, data (“D”) described here is quantitative data (“QD”). Uncontradicted, any reference to data/D can be substituted with QD or vice versa, where doing so is clarifying or results in the provision of suitable alternative aspects. In cases where a type/category of data or particular item or collection of data can be or comprise information that is either or both qualitative or quantitative, such alternate forms should be considered to be implicitly disclosed as alternative aspects (alone or in combination with the explicitly mentioned form).

TERMS AND PRINCIPLES SPECIFIC TO THE TECHNOLOGY OR THIS DISCLOSURE

The following description of certain terms and acronyms is provided to assist readers in understanding the technology. Additional acronyms may be only provided in other parts of this disclosure, and acronyms that are well known in the art may not be provided here.

The “RVS patents” means WO 2018/111945 and patent documents related thereto (e.g., US 2023/0184645 and US 2018/0306031), US 2020/0408732, US 2021/0341455, US 2022/0283138, US 2023/0175369, the patent documents described in the Background, and patent documents related thereto, in each case either by claim(s) of priority thereto or by sharing common claim(s) of priority therewith.

Uncontradicted, terms such as “rock volatiles” can be understood using the descriptions provided elsewhere here or in the RVS patents. Briefly, uncontradicted, the term “rock volatiles” generally means compounds that can be detected in geologic materials comprising rocks or extracted from such geologic materials using gentle vacuum extraction or a gentle vacuum-equivalent force/condition (a cause of release/extraction) and thereafter detected.

Uncontradicted, “volatile” and similar terms in this respect mean readily vaporizable (vaporizable at suitable levels) under the conditions performed to detect or extract and detect the applicable compounds (e.g., gentle vacuum extraction).

“Gentle vacuum” and “gentle vacuum extraction” are, uncontradicted, terms that refer to the use of low-pressure vacuum to extract rock volatiles, e.g., in accordance with the teachings of RVS patents, e.g., WO 2018/111945 (e.g., extraction of rock volatiles by application of a vacuum of between about 2 millibars and about 20 millibars for e.g., 0.25-10 minutes, such as 0.5-7.5 minutes or 0.5-5 minutes). In general, a suitable gentle vacuum is strong enough to extract/obtain a suitable amount of RVs for the applicable analysis but does not significantly, sizably, etc., destroy or chemically modify the RVs or cause the release of interfering/contaminating compounds that might interfere with accurate measurement of the RVs (e.g., structural compounds-such as structural CO₂ or structural water, etc., as applicable or materials in fluid inclusions, etc.).

Gentle vacuum may be low extraction force gentle vacuum (e.g., application of a vacuum of ˜10-30 millibars, such as 15-25 millibars, e.g., around 20 millibars) or a high extraction force gentle vacuum (e.g., application of a vacuum of about 1-3 millibars, such as 1.5-2.5 millibars, e.g., around 2 millibars).

Terms such as “gentle vacuum equivalent” or “gentle vacuum extraction equivalent” mean a force/condition that achieves significantly similar results to gentle vacuum extraction when applied to the same or similar material. A gentle vacuum equivalent could include, e.g., chemical extraction, application of positive pressure, or another type of force or condition that causes the extraction of a similar amount or approximately the same amount (or at least otherwise suitable amount) of rock volatiles as does/would gentle vacuum extraction. Uncontradicted, any method described herein as being performed with a gentle vacuum or a type of gentle vacuum provides implicit disclosure of an aspect in which RV extraction is performed using an alternative extraction technique that can be characterized as a gentle vacuum equivalent.

A “pressure quantitative rock volatile” (a “PQRV”) means a rock volatile compound (aka, a “volatile” or “rock volatile”) that, when measured in a suitable amount under suitable conditions (according to the principles in this disclosure), quantitatively measures the pressure of a portion of a geologic unit (GU). Examples of PQRVs include carbon dioxide (CO₂ or CO2), and carbon dioxide-related compound(s) (CDRC(s)) (e.g., bicarbonate) (described in the Background), and hydrogen sulfide (H₂S or H₂S). In aspects, the PQRV is limited to CO₂/CO2/CD (provided that other volatiles may be collected for other reasons, such as PMAFI rock volatiles). In aspects, the PQRV is limited to “easily extracted rock volatile carbon dioxide” or “EERVCD,” which is described above and referenced in the list of acronyms below. PQRVQD is quantitative data regarding PQRV(s). PQRVQD-derived data is, uncontradicted, data comprising, derived from, or based on PQRV (e.g., data relating PQRVQD to PMAFQD). Uncontradicted, any reference to PQRVD or PQRVQD herein provides support for a corresponding aspect in which the applicable data is PQRVQD-derived data (PQRVQDDD) and vice versa (e.g., data suitably/better factoring in 1, 2, or more PMAFQD(s) with PQRVQD).

“Pressure affecting factor(s)” (“PMAF(s)”) include any factors that, when present, are associated with a significant, sizable, or major change in pressure or other state/characteristic of an FGR. A PMAF can include a structural element of a geologic unit (e.g., a fault, conduit, etc.), a geologic material besides the resource-of-interest (e.g., water), an event element associated with a geologic unit (e.g., prior extraction of resource(s)), geologic unit geology element(s) (e.g., porosity), geologic unit chemistry (e.g., salinity), etc., or resource/location/site temperature. A PMAF indicator (“PMAFI”) is a measurable object or state that indicates the presence of PMAF(s). For example, organic acid(s), e.g., formic acid, acetic acid, or both, may be PMAFIs indicating the presence of biogenic activity. Other PMAFIs comprise rock volatile sulfur monoxide and rock volatile water (e.g., in aspects, only easily extracted water (“EEW”)). Quantitative data regarding PMAF(s) or PMAFI(s) may be referred to as PMAFQD herein.

A “quantitative analytical model” (“QAM”) or more generally a “model” can be any type of relationship of the quantity of one more PQRV(s), optionally combined with data associated with one, two, or more PMAFI(s)/PMAF(s), to the pressure of at least a portion of at least one geologic unit/GU type, such as a resource (e.g., an FGR) that quantifies geologic unit pressure (GUP) based on PQRV data (PQRVD) in a reliable manner. A QAM/model can be or can comprise any suitable mathematical model (e.g., a regression model (e.g., linear (e.g., least square), polynomial, or logistic regression), graphical/special model, etc. A QAM may be modified by inclusion of other factors (e.g., PMAF/PMAFI data), removal of some PQRVD (e.g., outliers, data from substantially different sites, etc.), or both, to generate a QAM with detectably, sizably, majorly, or significantly enhanced reliability (an enhanced quantitative analytical model or EQAM), which ideally provides pressure measurements from PQRVD that are highly reliable. The QAM indicator usually means the model has been demonstrated (validated) to provide reliable pressure measurements in at least some contexts. The EQAM indicator is used to simply refer to a model that provides an at least detectable improvement in reliability or fit over a QAM. The term “model” can, in aspects, be used to refer to/mean any type of a model that relates PQRVD, directly or through derivative(s) (e.g., a relationship of PQRVQD to non-PQRV FAPIRVQD), to pressure in at least portion(s) of geologic unit(s) (e.g., geologic fluid resource(s)). However, the term “model” is sometimes used to refer to a particular model or a particular class of model that is the focus of an aspect of disclosure (e.g., the term “model” might primarily be used to refer to model(s) that are characterizable as putative (unproven), reliable, or highly reliable (or unreliable) (reliability and high reliability are described below). So to avoid confusion and inadvertent loss of scope, readers will understand that, uncontradicted, the term model can refer to any suitable class or type of model in the applicable context of the technology/disclosure and that, uncontradicted, any particularly exemplified or referenced class of model in an aspect provides implicit support for a corresponding aspect in which that explicitly named class of model is substituted by another suitable model (e.g., where a reliable QAM is named in an aspect such disclosure provides support for a corresponding aspect where the term QAM is replaced with EQAM, which may be more reliable (e.g., sizably or significantly more reliable) than the QAM)

“Reliable” pressure measurements in a model are measures that have a degree of reliability of or that is or would be considered similar/substantially similar or equivalent to an R2 measure in a LINEST model/regression of at least about 0.7. A reliable model is associated with such a level of reliability. As discussed elsewhere, the term “reliability” is in reference to “fit” or strength of input data (either fit of PQRVQD/PQRVQD-derived data to direct pressure measurements or the fit of certain PQRVQD/PQRVQD-derived data to a larger/other

PQRVQD/PQRVQD-derived data set, in either case in an applicable model).

“Highly reliable pressure measurements” (or HRPMs) are measures that are associated with a degree of reliability or a model with a relatively high degree of reliability (e.g., any measure of or similar to an R2 measure in a LINEST test/function of at least about 0.85).

Uncontradicted, any disclosure of a “reliable” model provides implicit support for a corresponding aspect where the model is characterized as being highly reliable.

Uncontradicted, the term “proxy” means a composition (e.g., compound) that is an indicator of the quantity of another composition (e.g., compound), through any suitable relationship (e.g., mass spectrometry measured RV SO is a proxy for GM-associated sulfate).

“Associated condition data” or “ACD” means data associated with the condition of locations, the GU, a part of the GU, or GMs/samples from which PQRVDs (and possibly also PMAFIs/PMAFs) are measured. In aspects, the method comprises evaluating ACD in connection with the performance of the method to determine the applicability/fit of PQRVD to an existing QAM/model. For example, the method may include consideration of the nature of geologic resources in the evaluated GU (region, field, site, etc.), the nature of the GMs evaluated, etc. For example, as exemplified herein, presence of a substantial amount of water in a GU is an ACD that is a factor that indicates an evaluation of relationship(s) (e.g., the ratio) of rock volatile carbon dioxide (RVCD) quantity to rock volatile water (RVW) quantity should be considered as part of an enhanced QAM in evaluating how the quantification of pressure from PQRVD should be achieved.

“About” a location mean an area that is about the same specific location, meaning a location which provides a sizably similar or significantly similar result(s) as the specific location, is within a zone that is less than 1.5× the size of the space/zone that defines the specific location, or that varies from the zone/area of the specific location by less than 33% of the distance between the specific location and the nearest specific location or less than 33% of the average distance between specific locations.

Uncontradicted, “input” or “input data” when referring to applying data to a model or generating a model means the data that is applied to a model or inputted to (used as a factor) in the model. In aspects, a larger data set may be generated in methods and then refined (e.g., by relating data to another factor or by removing data points from the initial data set). Thus, input data may mean, in aspects, a data set that comprises modified data points, modified data forms, or reduced data points from a larger initial or parent data set. Herein, as an example, PQRVQD, PQRVQDDD, each with or without PMAFQD, can be input or input data.

Uncontradicted, the term “collecting” when used in reference to the collection of material, e.g., one or more rock material samples, provides support for specifically obtaining the one or more rock material samples/materials from a specific geologic location, as well as, e.g., simply gathering materials which were previously specifically obtained from a specific geologic location but which is/are presently in one or more other locations, such as, e.g., in one or more storage locations. Thus, “collecting” as it pertains to sample(s), e.g., rock material sample(s), can mean, e.g., directly obtaining sample(s) on site from a geologic unit or, e.g., obtaining previously obtained sample(s) having been housed elsewhere from the time of their collection to the present. Further, uncontradicted, the term “collecting” when used in reference to data derived/derivable from material(s) encompasses (concurrently implicitly discloses) both specifically performing one or more analysis(es) to generate such data as well as, e.g., simply gathering such data previously generated, such as, e.g., from a stored program, from one or more other data sources such as one or more other laboratory (ies), publication(s), or any other data repository (ies) having such data available. Accordingly, method(s) described herein can comprise, e.g., use and application of sample(s) (e.g., rock material sample(s)) obtained directly from a geologic unit by practitioner(s) of the method; use and application of sample(s) (e.g., rock material sample(s)) previously obtained and which, e.g., have been stored from the time of their original collection (by practitioner(s) or other(s)) to the point in time in which they are used in method(s) herein; generation of data for use in model(s) herein; use of previously existing data (originally generated by practitioner(s) of method(s) herein or other(s)) in model(s) herein; or any combination(s) thereof. While in certain aspects, method(s) can comprise only immediately collected and newly generated data, or, e.g., only previously collected and previously generated data, in certain aspects, method(s) can comprise use of both immediately collected and previously collected sample(s); newly generated and previously generated data; and combination(s) thereof.

ACRONYMS

The following is a list of acronyms used frequently in this disclosure, which is provided for the convenience of readers.

TABLE 1
Select Commonly Used Acronyms

Acronym	Full Term	Brief Description
ACD	Associated condition	Described above, a term that means data associated with
	data	the condition of locations, the GU, a part of the GU, or
		GMs/samples from which PQRVDs (and possibly also
		PMAFIs/PMAFs) are measured.
CCS	Carbon capture	Known term referring to the storage of carbon in
	sequestration/storage	subterranean structures/environments.
CCUS	Carbon capture,	Known term referring to the combined utilization of
	utilization, and storage	geologic resources and storage of carbon in related
		subterranean structures/environments.
CDRC	Carbon dioxide-	A CO2-associated compound that can be indicative of
	related compound	GFR or geologic unit pressure. Described in the
		Background and associated RVS Patents.
DPM	Direct pressure	A form of pressure measurement distinct from
	measurement	PQRVQD or PQRVQD-derived pressure measurements
		(e.g., known pressure testing method(s)) used to
		quantify borehole geologic pressure, such as, e.g. DST.
DST	Drill stem test	Known pressure testing method used to quantify
		borehole geologic pressure (described in Background).
EERVCD	Easily extracted rock	Rock volatile carbon dioxide (CO2/CD)
	volatile carbon	(RVCD/RVCO2) that is extractable from rock material
	dioxide	by applying a gentle vacuum at a pressure of ≥6
		millibars, e.g., ≥10 millibars, such as about 10 to about
		200 millibars, such as about 10 to about 100 millibars.
EEW	Easily extracted water	Rock volatile water that is extracted by “easy” (gentle)
		extraction methods
EQAM	Enhanced QAM	A model that is improved over a QAM, typically by
		factoring in PMAFD/PMAFID, removal of selected
		PQRVD, or both
GFR	Geologic fluid	A resource (e.g., a reserve) that comprises, and often is
	resource	mostly, generally, substantially, or entirely composed of
		a fluid (e.g., a gas, a liquid, or both).
GM	Geologic material	A term referring to any rock or rock-derived/interacting
		material (e.g., drilling mud) or material present in
		subterranean environments or derived from such
		material or used in geologic resource utilization. This
		term is known in the art and exemplified/described
		elsewhere here. GM that comprises, mostly comprises,
		generally consists of, or consists of rock may be
		described as rock material (RM). Uncontradicted, each
		term provides implicit disclosure of corresponding
		aspects in which the term is substituted with the other.
GR	Geologic resource	A term known in the art and characterized in the
		Background.
GU	Geologic unit	A geographical area or a 3-dimensional subterranean
		space that can be defined by features, structures, or
		current/prior contents or by one or more
		coordinates/relevant measures and that contains many
		(e.g., at least 100s or at least 1000s SLs). Examples of
		GUs are provided throughout (see, e.g., the
		Background).
GUSL	Geologic unit specific	A specific location in a geologic unit. It is a synonym
	location	for a specific location.
GUP	Geologic unit pressure	Pressure in a location or portion of a geologic unit, such
		as in a resource, e.g., a geologic fluid (GF) resource
		(GFR)
HRPM	Highly reliable	A pressure measurement made from PQRV data using a
	pressure measurement	model (alone or as modified by inclusion of
		PMAF/PMAFI data or modification of the PQRVD
		inputted/fed to the model) that has an R2 value in a
		LINEST/linest function of at least 0.85 or a similar
		degree of reliability/fit.
PMAF	Pressure affecting	Factor(s) that detectably, significantly, or
	factor(s)	sizably/majorly change the pressure of or are associated
		with a resource or that detectably, significantly, or
		sizably enhance a model
PMAFI	PMAF-indicator	An object (e.g., a compound) (other than a PQRV) or a
		state/condition that indicates the presence of a PMAF
		and that is associated with a significant, sizable,
		detectable, or major change in GU/GUL pressure, model
		enhancement, or both (e.g., rock volatile sulfur
		monoxide, water, or both).
PMAFIRV	PMAFI rock volatile	A PMAFI that is a rock volatile (and a quantitative
(PMAFIRVQD)	(and PMAFIRV	measurement thereof)
	quantitative data)
PMAFQD	PMAF/PMAFI	A quantitative measurement associated with a PMAF
	quantitative data	(e.g., with a PMAFI) that is suitable for inclusion in a
		model, and typically leads to a detectable, sizable, or
		major change in the reliability of the model.
PAM	Putative analytical	A model that is proposed to establish an at least reliable
	model	pressure quantitation based on PQRVD
PQRV	Pressure quantitative	A rock volatile that can provide a reliable approximate
	rock volatile	or similar quantitative pressure measurement when
		suitably measured and analyzed (e.g., EERVCD). As
		exemplified/described here, the suitability of PQRV
		measurement may mean factoring in one or more
		PMAFI(s), e.g., by creation of a ratio or other
		relationship between the PQRV and PMAFI (e.g., a ratio
		of carbon dioxide to EEW, SO, or both).
PQRVD	PQRV data	Data (typically quantity of compound) for a PQRV from
		a location or sample.
PQRVQDDD	PQRV quantitative	Data derived from PQRV quantitative data (PQRVQD).
	data derivative data
QAM	Quantitative analytical	A model that accurately determines/predicts geologic
	model	unit (e.g., resource) pressure from quantities of
		PQRV(s), sometimes in association with
		PMAF(s)/PMAFI(s).
RM	Rock material	Geologic material (GM), mostly comprising, generally
		consisting of, substantially consisting of, consisting
		essentially of, or consisting entirely of rock.
		Uncontradicted, each term (RM and GM) provides
		implicit disclosure of corresponding aspects in which the
		term is substituted with the other.
RRRVCD	Release resistant rock	RVCD that is extractable from rock material by
	volatile carbon	applying a gentle vacuum pressure that is stronger than
	dioxide	gentle exaction suitable for release of EERVCD,
		typically that is extracted by a pressure of about 4
		millibars or less, e.g., 3 millibars or less, or 2.5 millibars
		or less.
RV	Rock volatile	“Volatile” (RV) is a term described above and in the
and	and	RVS Patents, but generally means a compound that is
RVD	RV derivative	extractable from rock or other geologic material using
		gentle vacuum extraction. An RV can be extracted from
		material comprising rock material or from other
		geologic material, such as drilling muds.
		An RVD is a compound derived from an RV as
		described elsewhere.
RVS	Rock	Any of the various methods described in the RVS
	volatiles	Patents, typically employing, i.a., gentle vacuum RV
	stratigraphy	extraction
SL	Specific location	A portion of a GU that is defined by one or more
		coordinates or relevant measurements that define its
		location within the GU.
SO or SO-	Sulfur monoxide	A chemical known in the art. SO is typically rare in
	(sulfur monoxide	natural environments but may be produced by applying
	anion)	mass spectrometry to sulfate or other RVs. The
		formula/abbreviation SO and SO- can be considered to
		be interchangeable, as can the terms sulfur monoxide,
		sulfur monoxide anion, and sulfate when used in such
		contexts.
TDS	Total dissolved solids	A term known in the art

In some cases, descriptions of terms and/or acronyms are repeated one or more times in the following portions of the disclosure to aid readability. Other acronyms used in the disclosure may be only explained elsewhere or in the art (e.g., EEW means easily extracted (RV) water as explained in the RVS patents).

Readers will understand that such abbreviations or elements of such abbreviations can be combined with other abbreviations used in this disclosure without inclusion in this table or other set off of the term. For example, a capital D can be commonly used to refer to the term “data” in acronyms, and the abbreviation QD is used to indicate “quantitative data,” thus the abbreviations EERVCDQD and EERVCDD mean “EERVCD quantitative data” and “EERVCD data,” respectively (and, likewise, e.g., PMAFID means PMAFI “data”). The letter Q stands for quantity or quantities, such that PQRVQ(s) means PQRV quantity(ies), and a term such as “EERVCDQ” means easily extracted rock volatile carbon dioxide quantity. Readers can derive a similar understanding from a review of common acronyms listed above or used elsewhere.