Prediction on rock strength by mineral composition from machine learning of ECS logs
Rock strength evaluation is critical in oil and gas exploration, but traditional methods, such as empirical formulas, laboratory tests, and numerical simulations, often struggle with accuracy, generalizability, and alignment with field conditions. This study proposes the use of Random Forest and Tra...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Energy Geoscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759225000071 |
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| author | Dongwen Li Xinlong Li Li Liu Wenhao He Yongxin Li Shuowen Li Huaizhong Shi Gaojian Fan |
| author_facet | Dongwen Li Xinlong Li Li Liu Wenhao He Yongxin Li Shuowen Li Huaizhong Shi Gaojian Fan |
| author_sort | Dongwen Li |
| collection | DOAJ |
| description | Rock strength evaluation is critical in oil and gas exploration, but traditional methods, such as empirical formulas, laboratory tests, and numerical simulations, often struggle with accuracy, generalizability, and alignment with field conditions. This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. By utilizing the dry weight of minerals as input, the model predicts key mechanical properties, including Young's modulus, Poisson's ratio, bulk modulus, shear modulus, and uniaxial compressive strength. The findings demonstrate that mineral compositions, such as clay, quartz-feldspar-mica, carbonate, anhydrite, and pyrite, significantly influence rock strength. Specifically, clay content impacts Young's modulus, bulk modulus, and shear modulus, while quartz-feldspar-mica affects Poisson's ratio, and anhydrite is the primary factor influencing compressive strength. Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals, while negative correlations emerged with clay, pyrite, and quartz-feldspar-mica. The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency. Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later, making it highly suitable for well-logging interpretation. Although the Transformer model was less computationally efficient, it exhibited strengths in predicting subsurface strength parameters, particularly in capturing spatial variations and forecasting these parameters across different spatial locations. This study introduces a novel AI-driven approach to rock strength evaluation, bridging the gap between mineral composition and mechanical properties, with significant implications for resource extraction and reservoir management. |
| format | Article |
| id | doaj-art-abe6f4a94f99423e825bd4aa01d04da0 |
| institution | Kabale University |
| issn | 2666-7592 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy Geoscience |
| spelling | doaj-art-abe6f4a94f99423e825bd4aa01d04da02025-08-20T03:46:46ZengKeAi Communications Co., Ltd.Energy Geoscience2666-75922025-06-016210038610.1016/j.engeos.2025.100386Prediction on rock strength by mineral composition from machine learning of ECS logsDongwen Li0Xinlong Li1Li Liu2Wenhao He3Yongxin Li4Shuowen Li5Huaizhong Shi6Gaojian Fan7College of Artificial Intelligence, China University of Petroleum-Beijing, Beijing, 102249, China; Beijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, ChinaBeijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, ChinaBeijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, ChinaBeijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, China; Corresponding author.Beijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, ChinaState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing, 102249, ChinaState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing, 102249, ChinaBeijing Key Laboratory of Optical Detection Technology for Oil and Gas, China University of Petroleum, Beijing, 102249, China; Basic Research Center for Energy Interdisciplinary, College of Science, China University of Petroleum, Beijing, 102249, ChinaRock strength evaluation is critical in oil and gas exploration, but traditional methods, such as empirical formulas, laboratory tests, and numerical simulations, often struggle with accuracy, generalizability, and alignment with field conditions. This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. By utilizing the dry weight of minerals as input, the model predicts key mechanical properties, including Young's modulus, Poisson's ratio, bulk modulus, shear modulus, and uniaxial compressive strength. The findings demonstrate that mineral compositions, such as clay, quartz-feldspar-mica, carbonate, anhydrite, and pyrite, significantly influence rock strength. Specifically, clay content impacts Young's modulus, bulk modulus, and shear modulus, while quartz-feldspar-mica affects Poisson's ratio, and anhydrite is the primary factor influencing compressive strength. Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals, while negative correlations emerged with clay, pyrite, and quartz-feldspar-mica. The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency. Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later, making it highly suitable for well-logging interpretation. Although the Transformer model was less computationally efficient, it exhibited strengths in predicting subsurface strength parameters, particularly in capturing spatial variations and forecasting these parameters across different spatial locations. This study introduces a novel AI-driven approach to rock strength evaluation, bridging the gap between mineral composition and mechanical properties, with significant implications for resource extraction and reservoir management.http://www.sciencedirect.com/science/article/pii/S2666759225000071Elemental capture spectroscopy (ECS)Rock strength predictionMineral compositionRandom forestTransformer |
| spellingShingle | Dongwen Li Xinlong Li Li Liu Wenhao He Yongxin Li Shuowen Li Huaizhong Shi Gaojian Fan Prediction on rock strength by mineral composition from machine learning of ECS logs Energy Geoscience Elemental capture spectroscopy (ECS) Rock strength prediction Mineral composition Random forest Transformer |
| title | Prediction on rock strength by mineral composition from machine learning of ECS logs |
| title_full | Prediction on rock strength by mineral composition from machine learning of ECS logs |
| title_fullStr | Prediction on rock strength by mineral composition from machine learning of ECS logs |
| title_full_unstemmed | Prediction on rock strength by mineral composition from machine learning of ECS logs |
| title_short | Prediction on rock strength by mineral composition from machine learning of ECS logs |
| title_sort | prediction on rock strength by mineral composition from machine learning of ecs logs |
| topic | Elemental capture spectroscopy (ECS) Rock strength prediction Mineral composition Random forest Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S2666759225000071 |
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