SYSTEM AND METHOD FOR CHARACTERIZING NEAR-WELLBORE LITHOLOGY, MINERALOGY AND HETEROGENEITY

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application 63/722,353 filed Nov. 19, 2024.

TECHNICAL FIELD

The disclosed embodiments relate generally to techniques for characterizing the rock surrounding a horizontal wellbore. In particular, these embodiments identify the heterogeneity of the lithology and/or mineralogy around the wellbore using azimuthal gamma ray data.

BACKGROUND

Over the past two decades, unconventional resources became economic thanks to enabling technologies including horizontal drilling and hydraulic fracturing. The U.S. Energy Information Administration (EIA) estimates that in 2023, about 8.32 million barrels per day of crude oil were produced directly from unconventional resources in the United States, which accounted for about 64% of total U.S. crude oil production in 2023.

The success of a horizontal well depends on the overall well-path design and the successful wellbore placement within the target reservoir. Currently in the unconventional resource plays, multiple lateral wells are drilled from one pad site. Each lateral well can be as long as ten-thousand feet or more, and the target zone as thin as 10 feet. To accomplish the goal to stay within these thin target windows, over the length of these laterals, is an extremely challenging task. This is due to the subsurface and borehole-position uncertainties as well as geological heterogeneity inherent in these drilling projects.

Local geology, if not fully understood, is a trigger of many drilling incidents, often leading to non-productive time and cost overruns. Drilling through hard and interbedded formations can negatively impact drilling operations by reducing drilling rates, damaging bits, and requiring excessive steering corrections to penetrate or extricate the bit from the horizon. These geological features are often below the seismic resolution and become unpredictable further away from the well-characterized pilot well.

Near wellbore lithology could have significant impact on the efficiency of hydraulic fracturing. Fracturing stages placed in soft and clay-rich facies could lead to limited fracture propagation, pressure loss and a low net pressure, often known as shale choke. On the other hand, fracturing stages placed in hard and brittle facies show high net pressures and low near well bore pressure losses, consistent with well-developed fracture geometry in the far-field.

Based on above reasons, it is critical to characterize the heterogeneity in geology and lithology along horizontal well for better quantify the performance in geosteering, drilling, and hydrocarbon production to further improve the economy of unconventional resources.

Horizontal wells are rarely characterized in as much detail as in vertical and pilot wells. Majority of the data acquired along horizontal wells is based on measurement-while-drilling (MWD) systems that are intended primarily for geosteering purposes. Total gamma ray (GR), or omni-directional GR, is the most commonly available petrophysical measurement. Such GR is often combined with drilling parameters such as rate of penetration (ROP) and weight on bit (WOB) as inputs to predict probable near wellbore lithology, such as silicates (quartz-feldspar-mica, or QFM), clay, and carbonates.

There exists a need for improved characterization of horizontal wells.

SUMMARY

In accordance with some embodiments, a method of characterizing near-wellbore lithology, mineralogy, and heterogeneity including receiving azimuthal gamma ray data from a horizontal well; calculating a difference between an up-sector gamma ray signal and a down-sector gamma ray signal and normalizing the difference by an average gamma ray value to generate a gamma ray difference value; using the gamma ray difference value to identify heterogeneous intervals along the horizontal well to generate a heterogeneity index; and using the heterogeneity index for at least one of geomechanical analysis of wellbore stability, drilling performance analysis, fracturing decisions, and production performance diagnosis is disclosed. In an embodiment, the method may also segment an azimuthal gamma ray image to calculate a cross-section and total volume of hard rock the drill bit cut through. In an embodiment, additional data such as drilling parameters (WOB, ROP) and MWD data (continuous inclination and azimuth, shocking and vibration) may be used if available. In another embodiment, a machine-learning model may be used, wherein the machine-learning model was trained to predict mineralogy from gamma ray well logs, rate of penetration of a drill bit, and weight on the drill bit, and wherein the mineralogy is also used to generate the heterogeneity index.

In another aspect of the present invention, to address the aforementioned problems, some embodiments provide a non-transitory computer readable storage medium storing one or more programs. The one or more programs comprise instructions, which when executed by a computer system with one or more processors and memory, cause the computer system to perform any of the methods provided herein.

In yet another aspect of the present invention, to address the aforementioned problems, some embodiments provide a computer system. The computer system includes one or more processors, memory, and one or more programs. The one or more programs are stored in memory and configured to be executed by the one or more processors. The one or more programs include an operating system and instructions that when executed by the one or more processors cause the computer system to perform any of the methods provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example method for characterizing the heterogeneity around the well bore at each depth, and registering such heterogeneity depth bv depth along the horizontal well;

FIG. 2 illustrates a real-time GR image on the top track, up-, down- and average GR in the second track, and ΔGR % in the bottom track;

FIG. 3 illustrates a heterogeneity index defined by the exponential decay constant relating the change of lateral length with high ΔGR % versus the ΔGR % cutoffs;

FIG. 4 illustrates the calculation of σ_hard, the cross-section area of hard rock at each depth based on GR images;

FIG. 5 illustrates an example method for estimating the cross-section of hard rock σ_hard cut through by the drill bit at each depth, and accumulated volume of hard rock cut through by the drill bit by integration σ_hard with the thickness of each hard rock layer;

FIG. 6 illustrates individual azimuthal GR sectors that can be used as inputs for predicting lithology along the wellbore;

FIG. 7 illustrates a system for characterizing near wellbore mineralogy using machine learning;

FIG. 8 shows a result of a mineralogy prediction system, validated and bling tested; and

FIG. 9 shows a result of a mineralogy prediction system compared with the actual mineral volumes.

Like reference numerals refer to corresponding parts throughout the drawings.

DETAILED DESCRIPTION OF EMBODIMENTS

Described below are methods, systems, and computer readable storage media that provide a manner of characterizing near-wellbore lithology, mineralogy, and heterogeneity and drill-bit fatigue along horizontal wells. These embodiments are designed to be of particular use for wells drilled in unconventional (i.e., shale and other tight rock) formations.

Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure and the embodiments described herein. However, embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures, components, and mechanical apparatus have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

The methods and systems of the present disclosure may, in part, use one or more models that are machine-learning algorithms. These models may be supervised or unsupervised. Supervised learning algorithms are trained using labeled data (i.e., training data) which consist of input and output pairs. By way of example and not limitation, supervised learning algorithms may include classification and/or regression algorithms such as neural networks, generative adversarial networks, linear regression, etc. Unsupervised learning algorithms are trained using unlabeled data, meaning that training data pairs are not needed. By way of example and not limitation, unsupervised learning algorithms may include clustering and/or association algorithms such as k-means clustering, principal component analysis, singular value decomposition, etc. Although the present disclosure may name specific models, those of skill in the art will appreciate that any model that may accomplish the goal may be used.

Here we present the methods and workflows to characterize and display near wellbore lithology and heterogeneity along horizontal wells. This is enabled by azimuthal measurement acquired along horizontal wells. One of such measurements available is azimuthal GR.

The most common type of MWD petrophysical measurement is GR, also known as total GR, average GR, or omni-directional GR. Such GR along horizontal wells are impacted not only by near wellbore lithologies, but also by its proximity to interfaces between different stratigraphic layers. It is often insufficient to use omni-directional GR to predict near wellbore lithologies, especially when the wellbore is crossing thinly laminated beds between shale and carbonate.

For geosteering unconventional wells, the industry started to replace the standard omni-directional GR tools with azimuthal GR tools. The azimuthal GR tools provide azimuthal sensitivity to determine whether the wellbore is approaching the top or bottom of a geological feature. Azimuthal GR has been primarily used for geosteering purposes.

It is our objective here to better characterize near-wellbore lithology, around-the-wellbore and along-the-horizontal well heterogeneity, as well as drill-bit fatigue by accumulating the volume of hard rock the bit cutting through, using azimuthal wellbore measurements including azimuthal GR.

The new methods and workflows consist of the following components:

• • 1. Heterogeneity around the circumference of the wellbore can be characterized by azimuthal wellbore measurement.
    • a. For Azimuthal GR, the near wellbore lithology is heterogeneous when up- and down-sector of GR is different beyond level of tool noise and standoff difference.

Δ ⁢ GR = GR Up - GR Down ❘ "\[LeftBracketingBar]" Δ ⁢ GR ❘ "\[RightBracketingBar]" = ❘ "\[LeftBracketingBar]" GR Up - GR Down ❘ "\[RightBracketingBar]"

• • • b. Such difference is normalized to the average GR to serve as a heterogeneity value at each measured depth

Δ ⁢ GR ⁢ % = ❘ "\[LeftBracketingBar]" GR Up - GR Down ❘ "\[RightBracketingBar]" GR Avg

• • • c. ΔGR % can be used as a flag along horizontal wells to label highly heterogeneous intervals as illustrated in FIG. 1.
    • d. Depending on application, different ΔGR % cutoffs can be used to highlight certain intervals. FIG. 2 illustrates the real-time GR image on the top track, up-, down- and average GR in the second track, and ΔGR % in the bottom track. ΔGR % is color-filled by a chosen cutoff of 0.3 to highlight the highly heterogeneous intervals. Depending on application, different cutoffs can be chosen to highlight geological features.
  • 2. Heterogeneity along the wellbore can be characterized by integrating the depth interval with ΔGR % larger than certain cutoff, and normalized to the total logging interval
    • a. A heterogeneity index can be defined by the exponential decay constant relating the change of lateral length with high ΔGR % versus the ΔGR % cutoffs. An example is shown in FIG. 3.
  • 3. The azimuthal distribution of GR features can also be used to characterize how deep the borehole cuts into certain stratigraphic layers.
    • a. The volume of hard rock cut by the drill bit can be estimated by analyzing the borehole images for low GR carbonate signatures. The cross-section area of hard rock at each depth can be calculated as σ_hard, as shown in FIG. 4. The total volume of hard rock cut by the drill bit is given by:

Total ⁢ Volume ⁢ of ⁢ Hard ⁢ Rock = ∑ i = 0 n σ hard i · Δ ⁢ d i = σ hard 1 · Δ ⁢ d 1 + σ hard 2 · Δ ⁢ d 2 + σ hard 3 · Δ ⁢ d 3 ⁢ …

• • • b. Pattern recognition and image analysis algorithm including AI and machine learning methods can be used to accelerate the identification of hard rock and calculation of total volume of hard rocks.
    • c. Total volume of hard rock cut through by the drill bit can be recorded throughout its life of operation to analyze the drill bit fatigue and predict its remaining life. Drill bit close to its end of life will be replaced before next bit run to avoid unnecessary operations to replace drill bit.
  • 4. Near wellbore lithology and mineralogy can be better predicted by azimuthal GR.
    • a. Individual azimuthal GR sector can be used as inputs for predicting lithology along the wellbore. An example is shown below in FIG. 6. The lithology predicted using average GR as an input is significantly different from the lithology predicted using up- or down-GR, especially in the intervals with high level of azimuthal heterogeneity. The wellbore placed near the interface of low-GR carbonate and high-GR organic shale can be easily interpretated as quartz-rich mudstone with intermediate GR value.
    • b. Azimuthal GR can be used to improve lithology and mineralogy measurements based on cuttings as the wellbore crossing stratigraphic layer boundaries. Drill cuttings are broken pieces of formation rock thus they are a mixture of rocks the wellbore cutting through.
    • c. Azimuthal GR can be utilized to build geosteering cross section where the lithology further away from wellbore can be inferred from stratigraphic layers from pilot well.
  • 5. Improve near-wellbore mineralogy prediction by incorporating stratigraphic knowledge from vertical well
    • a. In the same geological region, there are vertical wells with open-hole well logs and/or core/cuttings measurement. Detailed mineralogy analysis based on well logs or core/cuttings can be performed to a high precision and accuracy.
    • b. Within a specific stratigraphic layer, detailed correlation and constraints between mineralogy and gamma rays can be established to improve the mineralogy prediction along horizontal wells.
  • 6. Improve near-wellbore mineralogy prediction by combining additional drilling parameters, measurement-while drilling (MWD), or logging-while-drilling (LWD) data whenever available
    • a. Rate of penetration (ROP) and weight on bit (WOB) can be combined with gamma ray data to better predict mineralogy because it is more difficult to drill through hard rock, which is often associated with carbonate lithologies.
    • b. Continuous inclination and azimuth, if acquired using MWD, can be used to characterize drill bit dynamics, wear and tear of the drill bit, in response to different lithology and lithology change. This can also be used to predict lithology in turn.
    • c. Shocking and vibration, if acquired using MWD, can be used to characterize the dynamics of drill bit and bottom-hole assembly (BHA) while drilling through formation of different lithology in different wellbore conditions. This can also be used to predict lithology and BHA reliability in turn.
    • d. In addition to gamma rays, additional MWD or LWD can be used to improve mineralogy predication are:
      • i. Omni-directional or azimuthal spectral gamma ray that provides uranium, thorium, and potassium concentrations
      • ii. Omni-directional or azimuthal density and photoelectric factor
      • iii. Omni-directional or azimuthal resistivity
      • iv. Neutron porosity
  • 7. Improve near-wellbore mineralogy prediction by integrating with a database of cuttings analysis as shown in FIG. 7.
    • a. Establish a database of mineralogy analysis from cuttings recovered from lateral sections for reservoir characterization.
    • a. This database was used to train a machine learning model to predict the volumes of three major mineralogical components: silicates (quartz-feldspar-mica or QFM), clay, and carbonates, using total GR or azimuthal GR whenever available, rate of penetration and weight on bit.
    • b. Apply validation and blind testing of mineralogy prediction model. In an example shown in FIG. 8, the validation and blind testing of the mineralogy prediction model showed mean arithmetic error and mean square errors comparable to the training dataset in 95% of the cases. The comparison between actual and predicted mineral volumes, as shown in FIG. 9, highlights the quality of the mineralogy prediction across example wells from training, validation and blind datasets.
    • c. When validated with production logs or similar surveillance, predicted mineralogy along the lateral can assist with differentiation of reservoir quality along the lateral, determination of production potential of the well, and optimization of the stimulation design to address geomechanical heterogeneity along the lateral.
  • 8. The application of this new method and workflow includes:
    • a. Geomechanical analysis of wellbore stability to assess the risk of wellbore failure
    • b. Drilling performance analysis on the interaction between drill bit/bottom-hole assembly and formations.
    • c. Engineered completion for placing fracturing stages in ductile rock or at least preferrable choose the ductile rock on top of the well and clay rich rock at the bottom of wellbore.
    • d. Production performance diagnosis that may be impacted by fracture growth and near wellbore lithology

While particular embodiments are described above, it will be understood it is not intended to limit the invention to these particular embodiments. On the contrary, the invention includes alternatives, modifications and equivalents that are within the spirit and scope of the appended claims. Numerous specific details are set forth in order to provide a thorough understanding of the subject matter presented herein. But it will be apparent to one of ordinary skill in the art that the subject matter may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

Although some of the various drawings illustrate a number of logical stages in a particular order, stages that are not order dependent may be reordered and other stages may be combined or broken out. While some reordering or other groupings are specifically mentioned, others will be obvious to those of ordinary skill in the art and so do not present an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.