SYSTEMS AND METHODS FOR WELL INTEGRITY EVALUATION

Description

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/720,314, filed on Nov. 14, 2024, the entire disclosure of which is incorporated herein by reference for all purposes.

TECHNICAL FIELD

This disclosure generally relates to systems and methods for well integrity evaluation.

BACKGROUND

Increasing government regulations are requiring oil and gas operators to provide strict assurances of zonal isolation throughout the full lifecycle of a well. The recent advent of technology for barrier evaluation through multiple pipe strings is enabling lower cost and emissions operations in this well integrity market. A barrier may be used for well completion, for example, to ensure that the well is secured after drilling operations have been completed at the site. Most notably for plug and abandonment operations, dual-string barrier evaluation technology is eliminating the costly need to first cut and pull an inner pipe before assessing cement condition using the prior standard single-string.

The dual-string barrier evaluation technology leverages multiple acoustic modalities from sonic and ultrasonic measurements. A prior invention disclosure has described specific methods for well integrity analyses using sonic measurements over depth intervals. These methods rely on estimation of slowness frequency phase dispersions from sonic measurements and labeling of specific modes based on modeled templates. The presence or absence of these specific modes is indicative of well properties important for barrier evaluation. To address the challenge of selecting appropriate models for the specific modes in the sonic measurement data, a recent invention disclosure describes methods for calibrating dispersion curve templates. An important remaining challenge that is addressed here is quantifying and visualizing how well the sonic measurement data agrees with template mode curves across depths, particularly when wellbore conditions vary.

As adoption of multi-string barrier evaluation technology grows, so do encounters with conditions in the field of operations that increase acoustic complexities. Variations in pipe geometries, formation and annular material properties, and interfacial conditions can all influence the sonic measurement dispersion quality. Consequently, indications of sonic dispersion quality and agreement with expected template models is important for robust barrier evaluation in varying field conditions.

Accordingly, there is a need for systems and methods for well integrity evaluation.

SUMMARY

This disclosure pertains to systems and methods for well integrity evaluation.

A first aspect of this disclosure pertains to a method for managing completion of a well, the method including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.

A second aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality image includes a projection image indicating a concentration of the dispersion image data in at least one vicinity of the template dispersion curve.

A third aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data in at least one vicinity of the template dispersion curve.

A fourth aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data with respect to slowness in at least one vicinity of the template dispersion curve.

A fifth aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data with respect to frequency in at least one vicinity of the template dispersion curve.

A sixth aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality metric includes a concentration measure based on a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve.

A seventh aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality metric includes a coverage measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.

An eighth aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality metric includes an intensity measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.

A ninth aspect of this disclosure pertains to the method of the first aspect, wherein the dispersion quality metric includes an integrated measure based on: a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve, and an intensity measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve.

A tenth aspect of this disclosure pertains to the method of the first aspect, and further includes: integrating the dispersion quality metric with a well property estimate, generating one or more flag outputs respectively representing threshold values, and determining a confidence value in at least one estimated well property for the well based on the one or more flag outputs.

An eleventh aspect of this disclosure pertains to the method of the tenth aspect, wherein the generating the one or more flag outputs includes generating an interpretation flag by combining and thresholding the dispersion quality metric with the well property estimate.

A twelfth aspect of this disclosure pertains to the method of the tenth aspect, wherein the generating the one or more flag outputs includes generating an agreement flag by differencing and thresholding the dispersion quality metric with the well property estimate.

A thirteenth aspect of this disclosure pertains to the method of the first aspect, and further includes: performing the well completion by disposing a well barrier in the wellbore to plug the well, wherein the obtaining the measurement data includes obtaining measurement data of the well barrier.

A fourteenth aspect of this disclosure pertains to one or more non-transitory computer-readable media storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.

A fifteenth aspect of this disclosure pertains to the one or more non-transitory computer-readable media of the fourteenth aspect, wherein the dispersion quality image includes a projection image indicating one or more of: a concentration of the dispersion image data in at least one vicinity of the template dispersion curve, a distribution of the dispersion image data in at least one vicinity of the template dispersion curve, a distribution of the dispersion image data with respect to slowness in at least one vicinity of the template dispersion curve, or a distribution of the dispersion image data with respect to frequency in at least one vicinity of the template dispersion curve.

A sixteenth aspect of this disclosure pertains to the one or more non-transitory computer-readable media of the fourteenth aspect, wherein the dispersion quality metric includes one or more of: a concentration measure based on a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve, or an intensity measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.

A seventeenth aspect of this disclosure pertains to the one or more non-transitory computer-readable media of the fourteenth aspect, wherein the dispersion quality metric includes an integrated measure based on: a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve, and an intensity measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve.

An eighteenth aspect of this disclosure pertains to the one or more non-transitory computer-readable media of the fourteenth aspect, wherein the operations further include: integrating the dispersion quality metric with a well property estimate, generating one or more flag outputs respectively representing threshold values, and determining a confidence value in at least one estimated well property for the well based on the one or more flag outputs.

A nineteenth aspect of this disclosure pertains to the one or more non-transitory computer-readable media of the eighteenth aspect, wherein the generating the one or more flag outputs includes one or more of: generating an interpretation flag by combining and thresholding the dispersion quality metric with the well property estimate, or generating an agreement flag by differencing and thresholding the dispersion quality metric with the well property estimate.

A twentieth aspect of this disclosure pertains to a system for a drilling operation, including: a well barrier in a wellbore of a well disposed in a geological formation, one or more processors, and at least one memory including at least one non-transitory computer-readable medium storing instructions that, when executed by at least one of the one or more processors, cause the system to perform operations, the operations including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

Additional features and advantages of embodiments of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such embodiments. The features and advantages of such embodiments may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features will become more fully apparent from the following description and appended claims or may be learned by the practice of such embodiments as set forth hereinafter.

BRIEF DESCRIPTION OF DRAWINGS

To describe the manner in which the above-recited and other features of the disclosure can be obtained, a more particular description will be rendered by reference to specific implementations thereof which are illustrated in the appended drawings. For better understanding, the like elements have been designated by like reference numbers throughout the various accompanying figures. While some of the drawings may be schematic or exaggerated representations of concepts, at least some of the drawings may be drawn to scale. Understanding that the drawings depict some example implementations, the implementations will be described and explained with additional specificity and detail through the use of the accompanying drawings.

FIG. 1 is a schematic view illustrating an example of a geologic environment.

FIG. 2 is a flow diagram of an example workflow according to an example embodiment of the present disclosure.

FIG. 3 is a set of images of pre-processing outputs according to an example embodiment of the present disclosure.

FIG. 4 is a set of images for dispersion quality outputs across depths according to an example embodiment of the present disclosure.

FIG. 5 is a flow diagram of an example workflow according to an example embodiment of the present disclosure.

FIG. 6 is a diagram of interpretation and agreement flags according to an example embodiment of the present disclosure.

FIG. 7 is a set of diagrams of field data information and interpretation according to an example embodiment of the present disclosure.

FIG. 8 is a flowchart for an example method.

FIG. 9 illustrates certain components that may be included within a computer system according to an example embodiment of the present disclosure.

Before explaining the disclosed embodiment of this disclosure in detail, it is to be understood that the invention is not limited in its application to the details of the particular arrangement shown, as the invention is capable of other embodiments. Example embodiments are illustrated in referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than limiting. Also, the terminology used herein is for the purpose of description and not of limitation.

DETAILED DESCRIPTION

While the subject disclosure applies to embodiments in many different forms, specific embodiments are shown in the drawings and will be described in detail herein with the understanding that the present disclosure is an example of the principles of the invention. It is not intended to limit the invention to the specific illustrated embodiments. The features of the invention disclosed herein in the description, drawings, and claims can be significant, both individually and in any desired combinations, for the operation of the invention in its various embodiments. Features from one embodiment can be used in other embodiments of the invention. In the description of the drawings, like reference numerals refer to like elements.

FIG. 1 is a schematic view illustrating an example of a geologic environment.

In the example of FIG. 1, an example geologic environment 150 may include layers (e.g., stratification) that may include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the one or more networks 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled at a wellsite for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

After drilling operations have been completed at a wellsite, during well completion, a casing pipe may be run into the well and cement placed may then be placed behind the casing pipe to secure the well and establish a barrier for zonal isolation to prevent uncontrolled fluid migration across various subsurface strata. Regulatory compliance usually requires the evaluation of the placement and bond quality of the cement behind the casing by deploying a cement evaluation tool inside the casing and logging the results over the depth zone. This is usually done either during the initial construction before commencement of well operations, as well as at the end-of-life decommissioning of the whole well (e.g., plug and abandonment) or a section thereof (e.g., slot recovery). Conventional cement evaluation tools work across a single casing. Conventional end-of-life barrier evaluation has hitherto required the removal of inner pipes present in operating wells, for example, either by pulling tubing or milling out the inner casings, adding substantially to the cost to the decommissioning. Therefore, the technology disclosed herein to evaluate barriers and assess the confidence therein across two or more casing strings can result in significant savings in cost and carbon footprint. Well construction can also benefit from such a technology as disclosed herein.

Example embodiments of the present disclosure may provide dipole answers of first reference explorations. Well integrity barrier evaluation may be determined using dispersion data. For example, a high dispersion value may indicate that the well is not bonded, and a low dispersion value may indicate that the well is bonded. Example embodiments may include verifying a barrier, and may also include placing the barrier before verifying it, and may also include repairing the barrier if it fails the validation.

Example embodiments of the present disclosure may provide quality control of sonic dispersion measurements given a model template. Example embodiments may provide an image-based approach for extracting and integrating sonic dispersion features, and may generate dispersion quality metrics and image projections that indicate mode presence, strength, and fit with respect to the template model across depths. Additionally, dispersion quality metrics may be used in conjunction with given estimates of the well property, for example, as an output to inform evaluation of a barrier condition.

Quality control (QC) indicators may be used to interpret and verify sonic measurement answers for dual-string barrier evaluation. Current dual-string barrier evaluation answers exploit sonic dispersion physics to identify the presence of a mode specified in a template. It can be difficult to determine if a modeled mode is present in sonic measurement field data when wellbore conditions vary. Indications of the targeted mode presence and fit with respect to a model template are important for correct assessment of barrier quality.

Example embodiments of the present disclosure may provide dispersion quality metrics and dispersion quality images that indicate sonic mode presence, strength, coverage, and fit with respect to a given model template. Additional flag outputs may combine an integrated dispersion quality control metric with the given well property estimates to aid interpretation and assure inferred well integrity properties.

Example embodiments may utilize a unique image-based sonic measurement processing approach for feature extraction and visualizations of dispersive modes given a model template. The logs may capture valuable information about how the dispersion image amplitudes are concentrated and spread across the template curve, without having to examine individual sonic depth frames. This is a different and complementary methodology to the previous approaches, which process the input data to generate a single log estimate curve. Example embodiments may generate QC outputs for a curve that aid interpretation and assurance of answers.

Example embodiments may provide an approach to assess presence and fit of a dispersive mode in sonic measurement data with respect to a model template. The output dispersion quality metrics may quantify strength of fit of the sonic measurement data to the model. Accompanying dispersion quality images may indicate the dispersive mode coverage, amplitude distribution and match to the template across depths.

FIG. 2 is a flow diagram of an example workflow according to an example embodiment of the present disclosure. FIG. 3 is a set of images of pre-processing outputs according to an example embodiment of the present disclosure. FIG. 4 is a set of images for dispersion quality outputs across depths according to an example embodiment of the present disclosure.

FIG. 2 shows a flow diagram of an example of a dispersion quality processing workflow 200 that may generate metrics and image outputs. FIG. 2 illustrates an overview of the dispersion quality processing workflow 200. The dispersion data and mode template curve inputs are converted to images during the pre-processing operations, examples of which are illustrated in FIG. 3. FIG. 3 shows an illustration of pre-processing outputs 300, for example, dispersion images 310 from estimates of slowness-frequency acoustic content and template images 320 from the dispersion mode template curve. Dispersion image features may be extracted and integrated to form the dispersion quality metrics output in path 1 of the workflow 200. The dispersion quality metrics may include dispersion intensity, concentration, and coverage in the vicinity of the template curve mode, as determined by the template images that may be generated during pre-processing.

At the same time, the dispersion images are also segmented and projected using the template images to produce dispersion quality images. The dispersion quality images output from path 2 include the fit and curve projections, as well as projections of dispersions across slowness and frequency in a vicinity of the template mode curve. The fit and curve projections are uniquely based on a template image with segmented cross sections and respectively provide log visualizations of the dispersion data concentration around the template mode and distribution along the curve. An illustration of the dispersion quality metrics and image projection outputs are provided in FIG. 4 with an accompanying interpretation. FIG. 4 is an illustration of dispersion quality outputs across depths, including metrics 410 and image projections 420. Corresponding interpretations 430 are noted next to representative depth frames 440 with the template curve overlaid in white, according to one or more example embodiments.

A more detailed description of the example dispersion quality processing workflow 200 shown in FIG. 2 is further explained below.

Inputs

To begin the dispersion quality processing, there may be two types of inputs available, for example, dispersion data 210 and a mode template curve 220.

The dispersion data may contain estimates of slowness-frequency dispersions from sonic measurements for each depth frame across a depth interval. A “matrix pencil” algorithm is one example of such an estimation algorithm that may be used to process the sonic measurements. The Matrix Pencil Method (MPM) is a signal processing approach employed for estimating dispersion characteristics from borehole acoustic measurements.

Meanwhile, the mode template curve 220 can be generated from modeling, which may be followed by any calibration operations, if applied. The template input may include curves in the slowness-frequency plane delineating expected mode dispersions indicative of a well property of interest for barrier evaluation.

Pre-Processing

Similarly, during the pre-processing operation, each of the inputs mentioned previously (e.g., the dispersion data 210 and the mode template curve 220) may be pre-processed differently. For the dispersion data input, a pre-processing operation may involve dispersion image generation 230 in which, for every depth frame, estimated dispersion data may populate a slowness-frequency grid with pre-defined bin sizes. Bins may represent discretized phase dispersions in slowness and frequency, and bin sizes may reflect both the sonic measurement parameters as well as the desired output resolution. The image bin amplitudes may capture the estimated energy, which may be normalized or weighted based on algorithm estimation confidence. Images may be accumulated over a specified depth interval, e.g., to increase the signal-to-noise ratio. Illustrations of dispersion images for depth frames are shown at 310 in FIG. 3.

As for the mode template curve 220 input, a pre-processing operation may involve template image generation 240 in which the template mode curves may be mapped to the slowness-frequency grid, e.g., with equal weighting for every bin that the curve occupies, as illustrated by the white line in the upper-left image in 320 of FIG. 3. Template image masks may be generated to isolate vicinities around the curve, for example, narrow and broad dilations in slowness and frequency, for dispersion quality processing. The amount of dilation is a parameter that should be set appropriately to capture regions of interest while avoiding nearby modes or arrivals. Mask dilation regions and cross section segmentations from a template model are illustrated on the bottom-right three images at 320 in FIG. 3.

The pre-processing operation for the mode template curve input may further include template cross-section extraction in which cross sections may be defined, for example, based on regions perpendicular to each point along the template curve in the slowness-frequency image regions. The extent of the cross sections may cover the template image dilation region. The cross sections may divide the region surrounding the template mode into two dimensions that are respectively parallel (e.g., along) and perpendicular (e.g., normal) to the curve.

As mentioned above with regard to FIG. 2, the dispersion quality processing can be broken down into two separate paths: path 1, which may include dispersion feature extraction and integration 250; and path 2, which may include dispersion template image projections 260.

Dispersion Quality Metrics Workflow: Path 1

In path 1, following a dispersion feature extraction operation, the dispersion images may be processed using the template images to extract features in vicinities (narrow and broad regions) surrounding the mode of interest. Features may include accumulated dispersion image intensity, amplitude concentration near the mode, and coverage across the template curve. Logs of these features may form the individual dispersion quality metrics, as illustrated in FIG. 4, which shows example depth frames and interpretations.

Next, in a feature integration operation, the extracted dispersion features may be combined into a single integrated dispersion quality metric, and a depth regularization may be applied to smooth over a specified interval. The dynamic range of the integrated dispersion quality metric may be constrained to be within 0-1. A representative example of the integrated dispersion quality metric is shown as a log in the FIG. 4 example. In the FIG. 4 illustration, higher values indicate strong mode presence with good fit to the template model.

Dispersion Quality Image Projections Workflow: Path 2

As for path 2, the process may begin with fit and curve projections, for example, to generate the fit and curve projections, and the template image cross-sections may be extracted for each frame, segmenting the dispersion image data. The fit projection may be generated by averaging the extracted cross sections for each frame, which may capture how the dispersion amplitudes are distributed close to and progressing away from the template mode curve. The output projection indicates how closely the dispersion data fits the template curve model across depths, including any mode shifts or variations from the center reference. An illustration of a fit projection is shown in FIG. 4 in which the best template match is indicated by amplitudes that are highly concentrated towards the center white line at zero, representing the template curve reference.

As each point of the curve projection is the sum along the corresponding cross section, the curve projection may thus capture the amplitude distribution across the template mode, indicating the observed mode strength and coverage in the dispersion data across depths. An example illustration of different dispersion distributions in the curve projection output is shown in FIG. 4.

The next operation may involve slowness and frequency projections. For example, the dispersion quality slowness projection image may be formed by summing the dispersion image amplitudes across frequencies in a vicinity near the template curve indicated by a template image. In a similar fashion, the dispersion quality frequency projection image may be formed by summing the dispersion image amplitudes across slownesses in the same vicinity indicated by the selected template image.

Output

Each path may have its own set of outputs. For example, path 1, which may include dispersion feature extraction and integration 250, may provide an output of dispersion quality metrics 270, and path 2, which may include dispersion template image projections 260, may provide an output of dispersion quality images 280. FIG. 4 illustrates an example of how attributes of the slowness phase dispersions for every frame may be represented in the output dispersion quality metrics and image projection logs.

FIG. 4 may be divided into four depth sections 450, 460, 470, and 480 representing regions of a well having increasing depth. A depth region at the top (section 450), which may have high dispersion amplitudes, may closely track the template mode curve 442, which appears in white in each of the dispersion images 440, and may yield an integrated dispersion quality metric at or near 1. The corresponding fit projection, in this example, is concentrated in the middle and the curve projection indicates a high amplitude distributed across the full mode extent. In the next depth region below (section 460), the observed dispersion amplitude has shifted upward, yielding lower dispersion quality metric values. For example, the concentration metrics may be reduced due to the dispersion energy concentration in a vicinity above the mode, rather than on top of the template curve. The fit projection shows a rightward shift and reduced amplitude in the curve projection indicates that some of the observed dispersion amplitude is too far from the template, outside of the narrow template image vicinity. The next depth region (section 470) shows mixed and noisy dispersion amplitudes that cross the template model curve but do not follow it closely or fully, as captured in relatively low dispersion quality metric values and noisy image projections with limited coverage. Finally, in the bottom depth region (section 480), when no mode is present, there is no observed dispersion and the quality metrics have zero values, while the projections indicate no dispersion amplitude.

Additional Processing

FIG. 5 is a flow diagram of an example workflow according to an example embodiment of the present disclosure. FIG. 6 is a diagram of interpretation and agreement flags according to an example embodiment of the present disclosure.

FIG. 5 shows an example of an additional processing workflow 500. The example workflow 500 may combine a dispersion quality metric 510 with a wellbore property estimate 520 to generate interpretation and agreement flag outputs that may help to infer wellbore properties. As illustrated in FIG. 5, an additional operation in QC processing, which may be a final operation in the QC processing, may be combining the integrated dispersion quality metric, hereafter referred to as a “QC metric,” with a wellbore property estimate to infer wellbore properties in a combination and thresholding operation 530. An interpretation flag may be generated (at 540), for example, by thresholding a combination of the QC metric and the wellbore property estimate. An agreement flag may be generated (at 550), for example, by thresholding the difference between the integrated dispersion quality metric and the wellbore property estimate, which may identify any depth regions in which the QC metric and wellbore property estimate do not agree. A depth regularization interval may be applied in generating the flag outputs.

FIG. 6 is an illustration of example interpretation flags 610 and agreement flags 620 generated by combining an integrated dispersion quality metric 630 with an wellbore property estimate input 640 for the same respective depth regions illustrated in FIG. 4. FIG. 6 provides an example illustration of the interpretation flags 610 and agreement flags 620, indicating regions in which the QC metric and estimate agree and help to infer well property presence or absence. In the lower noisy dispersion region, the wellbore property estimate is relatively high while the QC metric is low to moderate, resulting in a questionable assessment of the wellbore property.

Field Data Example

FIG. 7 is a set of diagrams of field data information and interpretation according to an example embodiment of the present disclosure.

FIG. 7 is a field data example 700 showing examples of dispersion images 710, dispersion quality metrics 720, dispersion quality image projections 730, a well property estimate 740 (e.g., by depth), a quality control (QC) metric 750, and flags 760. Some examples of flag keys are shown in FIG. 6. In the FIG. 7 example, an interpretation flag 762 and an agreement flag 764 are illustrated. The dispersion quality metrics 720 may include, for example, intensity 722, concentration 724, coverage 726, and an integrated QC metric 728. The dispersion quality image projections 730 may include, for example, fit 732, curve 734, slowness 736, and frequency 738.

In addition to metrics quantifying the dispersion quality and automated flags to aid interpretation, the image projections may provide rich information about dispersion data presence and distribution across the template mode, as indicated by the white overlay on the depth frame examples in 710, e.g., the white curved line in each image in 710. Frame locations are indicated as black circles on the depth log axis at 722 in top-to-bottom ordering reflecting the plots. FIG. 7 provides an example of outputs from the complete quality control processing suite in accordance with an example embodiment of the present disclosure. Examples of generated dispersion images for depth frames are shown at 710, with the template curve overlaid in white, e.g., the white curved line in each image in 710. Locations of the depth frames are indicated in 722 by black circles on the depth axis of the log outputs.

The dispersion quality metrics 720 may capture the intensity (722), concentration (724), and coverage (726) across the mode and may be integrated into an integrated QC metric 728, which may be used as the QC metric 750, which may then be combined with the well property estimate 740 to generate the flags 760, e.g., an interpretation flag 762 and a agreement flag 764. The dispersion quality images 730 may also provide visual indications of the dispersion data distribution and concentration around the template mode, which may be quantified in the accompanying metrics. The quality control processing outputs may provide valuable information to understand and interpret the well property estimate across depth regions. Example embodiments of the present disclosure may generate sonic dispersion quality metrics and image projections based on a template curve model.

FIG. 8 is a flowchart for an example method.

In FIG. 8, an example method 800 for managing completion of a well may include, at 810, obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed. The example method 800 may further include, at 820, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode. The example method 800 may further include, at 830, obtaining a dispersion image comprising dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes. The example method 800 may further include, at 840, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve. The example method 800 may further include, at 850, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image. The example method 800 may further include, at 860, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image. The example method 800 may further include, at 870, determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.

FIG. 9 illustrates certain components that may be included within a computer system according to an example embodiment of the present disclosure.

FIG. 9 illustrates certain components that may be included within a computer system 900, which may be used to control features according to embodiments of the present disclosure, such as the features discussed with reference to FIGS. 1-8. One or more computer systems 900 may be used to implement the various devices, components, and systems described herein.

The computer system 900 includes one or more processors 901. The processor(s) 901 may be a single processor or may include multiple processors and/or sub-processors. The processor(s) 901 may be a general-purpose single-or multi-chip microprocessor (e.g., an Advanced RISC (Reduced Instruction Set Computer) Machine (ARM)), a special-purpose microprocessor (e.g., a digital signal processor (DSP)), a microcontroller, a programmable gate array, etc. The processor(s) 901 may be referred to as a central processing unit (CPU). Although a single processor(s) 901 is shown in the computer system 900 of FIG. 9, in an alternative configuration, a combination of processors (e.g., an ARM and DSP) could be used. In one or more embodiments, the computer system 900 further includes one or more graphics processing units (GPUs), which can provide processing services related to both entity classification and graph generation.

The computer system 900 also includes memory 903 in electronic communication with the processor(s) 901. The memory 903 may be any electronic component capable of storing electronic information. For example, the memory 903 may be embodied as random access memory (RAM), read-only memory (ROM), magnetic disk storage media, optical storage media, flash memory devices in RAM, on-board memory included with the processor, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) memory, registers, at least one non-transitory computer-readable and/or processor-readable medium, and so forth, including combinations thereof. The memory may include a single memory device or multiple memory devices.

Instructions 905 and data 907 may be stored in the memory 903. The instructions 905 may be executable by the processor(s) 901 to implement some or all of the functionality disclosed herein. Executing the instructions 905 may involve the use of the data 907 that is stored in the memory 903. Any of the various examples of modules and components described herein may be implemented, partially or wholly, as instructions 905 stored in memory 903 and executed by the processor(s) 901. Any of the various examples of data described herein may be among the data 907 that is stored in memory 903 and used during execution of the instructions 905 by the processor(s) 901.

A computer system 900 may also include one or more communication interfaces 909 for communicating with other electronic devices. The communication interface(s) 909 may be based on wired communication technology, wireless communication technology, or both. Some examples of communication interfaces 909 include a Universal Serial Bus (USB), an Ethernet adapter, a wireless adapter that operates in accordance with an Institute of Electrical and Electronics Engineers (IEEE) 902.11 wireless communication protocol, a Bluetooth® wireless communication adapter, and an infrared (IR) communication port.

A computer system 900 may also include one or more input devices 911 and one or more output devices 913. Some examples of input devices 911 include a keyboard, mouse, microphone, remote control device, button, joystick, trackball, touchpad, and lightpen. Some examples of output devices 913 include a speaker and a printer. One specific type of output device that is typically included in a computer system 900 is a display device 915. Display devices 915 used with embodiments disclosed herein may utilize any suitable image projection technology, such as liquid crystal display (LCD), light-emitting diode (LED), gas plasma, electroluminescence, or the like, and may be provided in any desired number. At least one display controller 917 may also be provided, for converting data 907 stored in the memory 903 into text, graphics, and/or moving images (as appropriate) shown on the display device 915.

The various components of the computer system 900 may be coupled together by one or more buses, which may include a power bus, a control signal bus, a status signal bus, a data bus, etc. For the sake of clarity, the various buses are illustrated in FIG. 9 as a bus system 919.

The following are sections in accordance with at least one embodiment of the present disclosure:

• • Clause 1: A method for managing completion of a well, the method including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.
  • Clause 2: The method of clause 1, wherein the dispersion quality image includes a projection image indicating a concentration of the dispersion image data in at least one vicinity of the template dispersion curve.
  • Clause 3: The method of clause 1, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data in at least one vicinity of the template dispersion curve.
  • Clause 4: The method of clause 1, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data with respect to slowness in at least one vicinity of the template dispersion curve.
  • Clause 5: The method of clause 1, wherein the dispersion quality image includes a projection image indicating a distribution of the dispersion image data with respect to frequency in at least one vicinity of the template dispersion curve.
  • Clause 6: The method of clause 1, wherein the dispersion quality metric includes a concentration measure based on a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve.
  • Clause 7: The method of clause 1, wherein the dispersion quality metric includes a coverage measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.
  • Clause 8: The method of clause 1, wherein the dispersion quality metric includes an intensity measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.
  • Clause 9: The method of clause 1, wherein the dispersion quality metric includes an integrated measure based on: a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve, and an intensity measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve.
  • Clause 10: The method of clause 1, further including: integrating the dispersion quality metric with a well property estimate, generating one or more flag outputs respectively representing threshold values, and determining a confidence value in at least one estimated well property for the well based on the one or more flag outputs.
  • Clause 11: The method of clause 10, wherein the generating the one or more flag outputs includes generating an interpretation flag by combining and thresholding the dispersion quality metric with the well property estimate.
  • Clause 12: The method of clause 10, wherein the generating the one or more flag outputs includes generating an agreement flag by differencing and thresholding the dispersion quality metric with the well property estimate.
  • Clause 13: The method of clause 1, further including: performing the well completion by disposing a well barrier in the wellbore to plug the well, wherein the obtaining the measurement data includes obtaining measurement data of the well barrier.
  • Clause 14: One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.
  • Clause 15: The one or more non-transitory computer-readable media of clause 14, wherein the dispersion quality image includes a projection image indicating one or more of: a concentration of the dispersion image data in at least one vicinity of the template dispersion curve, a distribution of the dispersion image data in at least one vicinity of the template dispersion curve, a distribution of the dispersion image data with respect to slowness in at least one vicinity of the template dispersion curve, or a distribution of the dispersion image data with respect to frequency in at least one vicinity of the template dispersion curve.
  • Clause 16: The one or more non-transitory computer-readable media of clause 14, wherein the dispersion quality metric includes one or more of: a concentration measure based on a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve, or an intensity measure based on slowness-frequency energy content in at least one vicinity of the template dispersion curve.
  • Clause 17: The one or more non-transitory computer-readable media of clause 14, wherein the dispersion quality metric includes an integrated measure based on: a comparison of slowness-frequency energy content in at least one vicinity of the template dispersion curve to slowness-frequency energy content in a second vicinity of the template dispersion curve, a coverage measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve, and an intensity measure based on the slowness-frequency energy content in the at least one vicinity of the template dispersion curve.
  • Clause 18: The one or more non-transitory computer-readable media of clause 14, wherein the operations further include: integrating the dispersion quality metric with a well property estimate, generating one or more flag outputs respectively representing threshold values, and determining a confidence value in at least one estimated well property for the well based on the one or more flag outputs.
  • Clause 19: The one or more non-transitory computer-readable media of clause 18, wherein the generating the one or more flag outputs includes one or more of: generating an interpretation flag by combining and thresholding the dispersion quality metric with the well property estimate, or generating an agreement flag by differencing and thresholding the dispersion quality metric with the well property estimate.
  • Clause 20: A system for a drilling operation, including: a well barrier in a wellbore of a well disposed in a geological formation, one or more processors, and at least one memory including at least one non-transitory computer-readable medium storing instructions that, when executed by at least one of the one or more processors, cause the system to perform operations, the operations including: obtaining measurement data for a well completion in a geological formation in which a wellbore of a well is disposed, pre-processing the measurement data to obtain raw dispersion estimates of frequency slowness content of acoustic energy corresponding to at least one borehole mode, obtaining a dispersion image including dispersion image data using the raw dispersion estimates, the dispersion image data being based on an accumulation of raw estimated dispersion estimates of frequency-slowness content of borehole acoustic modes, generating at least one template image using a template dispersion curve, the at least one template image corresponding to at least one vicinity of the template dispersion curve, extracting a dispersion quality image of at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, extracting a dispersion quality metric of the at least one borehole mode corresponding to the template curve using the dispersion image and the at least one template image, and determining a confidence value for at least one estimated well property for the well based on the dispersion quality, the confidence value being determined using the template dispersion curve.

Systems and software, e.g., implemented on a non-transitory computer-readable medium, for performing the methods discussed herein are also within the scope of embodiments of the present disclosure.

Embodiments of the present disclosure may thus utilize a special purpose or general-purpose computing system including computer hardware, such as, for example, one or more processors and system memory. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures, including applications, tables, data, libraries, or other modules used to execute particular functions or direct selection or execution of other modules. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions (or software instructions) are physical storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the present disclosure can include at least two distinctly different kinds of computer-readable media, namely physical storage media or transmission media. Combinations of physical storage media and transmission media should also be included within the scope of computer-readable media.

Both physical storage media and transmission media may be used temporarily store or carry software instructions in the form of computer readable program code that allows performance of embodiments of the present disclosure. Physical storage media may further be used to persistently or permanently store such software instructions. Examples of physical storage media include physical memory (e.g., RAM, ROM, EPROM, EEPROM, etc.), optical disk storage (e.g., CD, DVD, HDDVD, Blu-ray, etc.), storage devices (e.g., magnetic disk storage, tape storage, diskette, etc.), flash or other solid-state storage or memory, or any other non-transmission medium which can be used to store program code in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer, whether such program code is stored as or in software, hardware, firmware, or combinations thereof.

A “network” or “communications network” may generally be defined as one or more data links that enable the transport of electronic data between computer systems and/or modules, engines, and/or other electronic devices. When information is transferred or provided over a communication network or another communications connection (either wired, wireless, or a combination of wired or wireless) to a computing device, the computing device properly views the connection as a transmission medium. Transmission media can include a communication network and/or data links, carrier waves, wireless signals, and the like, which can be used to carry desired program or template code means or instructions in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.

Further, upon reaching various computer system components, program code in the form of computer-executable instructions or data structures can be transferred automatically or manually from transmission media to physical storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in memory (e.g., RAM) within a network interface module (NIC), and then eventually transferred to computer system RAM and/or to less volatile physical storage media at a computer system. Thus, it should be understood that physical storage media can be included in computer system components that also (or even primarily) utilize transmission media.

One or more specific embodiments of the present disclosure are described herein. These described embodiments are examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, not all features of an actual embodiment may be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous embodiment-specific decisions will be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one embodiment to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

The articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements in the preceding descriptions. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. For example, any element described in relation to an embodiment herein may be combinable with any element of any other embodiment described herein. Numbers, percentages, ratios, or other values stated herein are intended to include that value, and also other values that are “about,” “˜”, or “approximately” the stated value, as would be appreciated by one of ordinary skill in the art encompassed by embodiments of the present disclosure. A stated value should therefore be interpreted broadly enough to encompass values that are at least close enough to the stated value to perform a desired function or achieve a desired result. The stated values include at least the variation to be expected in a suitable manufacturing or production process, and may include values that are within 5%, within 1%, within 0.1%, or within 0.01% of a stated value.

A person having ordinary skill in the art should realize in view of the present disclosure that equivalent constructions do not depart from the spirit and scope of the present disclosure, and that various changes, substitutions, and alterations may be made to embodiments disclosed herein without departing from the spirit and scope of the present disclosure. Equivalent constructions, including functional “means-plus-function” clauses are intended to cover the structures described herein as performing the recited function, including both structural equivalents that operate in the same manner, and equivalent structures that provide the same function. It is the express intention of the applicant not to invoke means-plus-function or other functional claiming for any claim except for those in which the words “means for” appear together with an associated function. Each addition, deletion, and modification to the embodiments that falls within the meaning and scope of the claims is to be embraced by the claims. Any trademarks mentioned herein are the property of their respective owners. Example embodiments are not limited to any particularly-mentioned products, trademarks, or properties.

The terms “approximately,” “about,” “˜”, and “substantially” as used herein represent an amount close to the stated amount that still performs a desired function or achieves a desired result. For example, the terms “approximately,” “about,” “˜”, and “substantially” may refer to an amount that is within less than 5% of, within less than 1% of, within less than 0.1% of, and within less than 0.01% of a stated amount. Further, it should be understood that any directions or reference frames in the preceding description are merely relative directions or movements. For example, any references to “up” and “down” or “above” or “below” are merely descriptive of the relative position or movement of the related elements.

The present disclosure may be embodied in other specific forms without departing from its spirit or characteristics. The described embodiments are to be considered as illustrative and not restrictive. The scope of the disclosure is, therefore, indicated by the appended claims rather than by the foregoing description. Changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.