Machine learning classification approach for formation delineation at the basin-scale
Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications. They are seen as novel methods that may enhance existing capabilities, providing for improved efficiency in exploration and production operations. Furthermore, their...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-06-01
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| Series: | Petroleum Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096249521000697 |
| _version_ | 1811325466941849600 |
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| author | Derek Vikara Vikas Khanna |
| author_facet | Derek Vikara Vikas Khanna |
| author_sort | Derek Vikara |
| collection | DOAJ |
| description | Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications. They are seen as novel methods that may enhance existing capabilities, providing for improved efficiency in exploration and production operations. Furthermore, their integration into reservoir management workflows may shape the future landscape of the energy industry. This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units (i.e., formations) as a function of total vertical depth and spatial positioning. The framework is applied in a case study to 13 specific formations of interest (Upper Spraberry through Atoka / Morrow reservoirs) in the Midland Basin, West Texas, United States; a prominent hydrocarbon producing sub-basin of the larger Permian Basin. The study dataset consists of over 275,000 records and includes data fields like formation identifier, true vertical depth (in feet) of formations observed, and latitude and longitude coordinates (in decimal degrees). A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training, validation, and testing. Four supervised learning approaches including random forest (RF), gradient boosting (GB), support vector machine (SVM), and multilayer perceptron neural network (MLP) were evaluated and their prediction accuracy compared. The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data. The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres, Upper Spraberry, Lower Spraberry, Clearfork, and Wolfcamp at 98, 94, 89, 94, and 94 percent respectively. Overall, the resulting data-driven model provides a robust, cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy applications. |
| first_indexed | 2024-04-13T14:33:46Z |
| format | Article |
| id | doaj.art-5bd41d94bebf472783201519b6c37fcb |
| institution | Directory Open Access Journal |
| issn | 2096-2495 |
| language | English |
| last_indexed | 2024-04-13T14:33:46Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum Research |
| spelling | doaj.art-5bd41d94bebf472783201519b6c37fcb2022-12-22T02:43:06ZengKeAi Communications Co., Ltd.Petroleum Research2096-24952022-06-0172165176Machine learning classification approach for formation delineation at the basin-scaleDerek Vikara0Vikas Khanna1Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States; KeyLogic Systems, LLC, Morgantown, WV, 26505, United StatesDepartment of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States; Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States; Corresponding author. Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, United States.Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications. They are seen as novel methods that may enhance existing capabilities, providing for improved efficiency in exploration and production operations. Furthermore, their integration into reservoir management workflows may shape the future landscape of the energy industry. This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units (i.e., formations) as a function of total vertical depth and spatial positioning. The framework is applied in a case study to 13 specific formations of interest (Upper Spraberry through Atoka / Morrow reservoirs) in the Midland Basin, West Texas, United States; a prominent hydrocarbon producing sub-basin of the larger Permian Basin. The study dataset consists of over 275,000 records and includes data fields like formation identifier, true vertical depth (in feet) of formations observed, and latitude and longitude coordinates (in decimal degrees). A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training, validation, and testing. Four supervised learning approaches including random forest (RF), gradient boosting (GB), support vector machine (SVM), and multilayer perceptron neural network (MLP) were evaluated and their prediction accuracy compared. The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data. The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres, Upper Spraberry, Lower Spraberry, Clearfork, and Wolfcamp at 98, 94, 89, 94, and 94 percent respectively. Overall, the resulting data-driven model provides a robust, cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy applications.http://www.sciencedirect.com/science/article/pii/S2096249521000697Permian basinMidland basinK-means clusteringRandom forestClassification machine learning |
| spellingShingle | Derek Vikara Vikas Khanna Machine learning classification approach for formation delineation at the basin-scale Petroleum Research Permian basin Midland basin K-means clustering Random forest Classification machine learning |
| title | Machine learning classification approach for formation delineation at the basin-scale |
| title_full | Machine learning classification approach for formation delineation at the basin-scale |
| title_fullStr | Machine learning classification approach for formation delineation at the basin-scale |
| title_full_unstemmed | Machine learning classification approach for formation delineation at the basin-scale |
| title_short | Machine learning classification approach for formation delineation at the basin-scale |
| title_sort | machine learning classification approach for formation delineation at the basin scale |
| topic | Permian basin Midland basin K-means clustering Random forest Classification machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2096249521000697 |
| work_keys_str_mv | AT derekvikara machinelearningclassificationapproachforformationdelineationatthebasinscale AT vikaskhanna machinelearningclassificationapproachforformationdelineationatthebasinscale |