Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China
For shale oil reservoirs in the Jimsar Sag of Junggar Basin, the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization. This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Petroleum Exploration and Development |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876380425606069 |
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| author | Yunjin WANG Fujian ZHOU Hang SU Leyi ZHENG Minghui LI Fuwei YU Yuan LI Tianbo LIANG |
| author_facet | Yunjin WANG Fujian ZHOU Hang SU Leyi ZHENG Minghui LI Fuwei YU Yuan LI Tianbo LIANG |
| author_sort | Yunjin WANG |
| collection | DOAJ |
| description | For shale oil reservoirs in the Jimsar Sag of Junggar Basin, the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization. This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application. A self-governing database capable of automatic capture, storage, calls and analysis is established. With this database, 22 geological and engineering variables are selected for correlation analysis. A separated fracturing effect prediction model is proposed, with the fracturing learning curve decomposed into two parts: (1) overall trend, which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance; and (2) local fluctuation, which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight. A policy gradient-genetic-particle swarm algorithm is designed, which can adaptively adjust the inertia weights and learning factors in the iterative process, significantly improving the optimization ability of the optimization strategy. The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters. The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells, and it has good practicability. |
| format | Article |
| id | doaj-art-e34e3d85e2924b95a647ae8163810a4b |
| institution | Kabale University |
| issn | 1876-3804 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum Exploration and Development |
| spelling | doaj-art-e34e3d85e2924b95a647ae8163810a4b2025-08-20T03:26:56ZengKeAi Communications Co., Ltd.Petroleum Exploration and Development1876-38042025-06-0152383084110.1016/S1876-3804(25)60606-9Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW ChinaYunjin WANG0Fujian ZHOU1Hang SU2Leyi ZHENG3Minghui LI4Fuwei YU5Yuan LI6Tianbo LIANG7State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China; Corresponding authorChina National Oil and Gas Exploration and Development Corporation, Beijing 100034, ChinaState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaChina National Petroleum Corporation, Beijing 100007, China; PetroChina Oil & Gas and New Energy Company, Beijing 100007, ChinaChina National Petroleum Corporation, Beijing 100007, ChinaPetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, ChinaState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaFor shale oil reservoirs in the Jimsar Sag of Junggar Basin, the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization. This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application. A self-governing database capable of automatic capture, storage, calls and analysis is established. With this database, 22 geological and engineering variables are selected for correlation analysis. A separated fracturing effect prediction model is proposed, with the fracturing learning curve decomposed into two parts: (1) overall trend, which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance; and (2) local fluctuation, which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight. A policy gradient-genetic-particle swarm algorithm is designed, which can adaptively adjust the inertia weights and learning factors in the iterative process, significantly improving the optimization ability of the optimization strategy. The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters. The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells, and it has good practicability.http://www.sciencedirect.com/science/article/pii/S1876380425606069Jimsar Sagshale oilfracturing parameterlearning curveintelligent optimizationreinforcement learning |
| spellingShingle | Yunjin WANG Fujian ZHOU Hang SU Leyi ZHENG Minghui LI Fuwei YU Yuan LI Tianbo LIANG Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China Petroleum Exploration and Development Jimsar Sag shale oil fracturing parameter learning curve intelligent optimization reinforcement learning |
| title | Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China |
| title_full | Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China |
| title_fullStr | Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China |
| title_full_unstemmed | Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China |
| title_short | Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China |
| title_sort | intelligent optimization method of fracturing parameters for shale oil reservoirs in jimsar sag junggar basin nw china |
| topic | Jimsar Sag shale oil fracturing parameter learning curve intelligent optimization reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S1876380425606069 |
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