Integrated Multiphysics Workflow for Automatic Rock Classification and Formation Evaluation Using Multiscale Image Analysis and Conventional Well Logs

González, Andrés; Kanyan, Lawrence; Heidari, Zoya; Lopez, Olivier

doi:10.30632/pjv61n5-2020a7

Cited by 4 publications

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“…Modern measurement devices acquire two-dimensional data over the whole surface of the well. Meaningful information from the resulting borehole images can then be extracted using powerful image processing tools (Gonzalez et al, 2020;Jiang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A contrario dip picking for borehole imaging

et al. 2021

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We have described an algorithm to perform automatic dip picking on borehole images. One key element of our method is a statistical validation, based on the a contrario theory, which is used to decide whether each candidate dip is to be accepted or not. Our method also uses a randomized Hough transform, which greatly improves the processing speed, allowing for a real-time detection of dips during image visualization. In addition, the same algorithm can be applied at different scales to provide a multiresolution analysis of the structures. Our experiments determine that our algorithm produces reliable dip picking by an evaluation on three manually annotated boreholes: Our method detects from 60% to 90% of the dips annotated by an expert, depending on the complexity of the data.

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Section: Introductionmentioning

confidence: 99%

A contrario dip picking for borehole imaging

et al. 2021

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“…There is a group at the university of Texas at Austin, which proposes a "multiphysics workflow" for lithology classification [10]. This workflow involves well logs, computed tomography (CT) scans, and core analysis data.…”

Section: Iia Related Work In Core Analysis and Automationmentioning

confidence: 99%

Board 90: Core Feature Extraction with Computer Vision

Vargas,

Torres,

Cruz

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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I received a bachelors of science in geological sciences from California State University Bakersfield (CSUB) the year of 2018. In 2020 I received a masters of science in geological sciences from CSUB with an emphasis in petroleum geology, and am currently pursuing a third degree in computer science. I am interested in research that helps the energy industry through the involvement of artificial intelligence.

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Integration of 3D Volumetric Computed Tomography Scan Image Data with Conventional Well Logs for Detection of Petrophysical Rock Classes

González

Heidari

Lopez

2021

SPE Reservoir Evaluation &Amp; Engineering

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Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

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Integrated Multiphysics Workflow for Automatic Rock Classification and Formation Evaluation Using Multiscale Image Analysis and Conventional Well Logs

Cited by 4 publications

References 0 publications

A contrario dip picking for borehole imaging

A contrario dip picking for borehole imaging

Board 90: Core Feature Extraction with Computer Vision

Integration of 3D Volumetric Computed Tomography Scan Image Data with Conventional Well Logs for Detection of Petrophysical Rock Classes

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