Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs
Abstract The cost of implementing improved oil recovery measures after the formation of dominant seepage channels is high, and the effectiveness is often not significant. By predicting the formation of dominant seepage channels and actively intervening in their early stages, it is possible to reduce...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-025-01984-y |
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| _version_ | 1850105632139509760 |
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| author | Chen Liu Zenghua Zhang Wensheng Zhou Chengyu Luo Lei Jiang |
| author_facet | Chen Liu Zenghua Zhang Wensheng Zhou Chengyu Luo Lei Jiang |
| author_sort | Chen Liu |
| collection | DOAJ |
| description | Abstract The cost of implementing improved oil recovery measures after the formation of dominant seepage channels is high, and the effectiveness is often not significant. By predicting the formation of dominant seepage channels and actively intervening in their early stages, it is possible to reduce development costs and improve oil recovery factor. Consequently, the prediction of dominant seepage channels has emerged as a key focus of research in reservoir engineering. This research introduces a novel method for predicting dominant seepage channels, termed Label Matrix of Seepage Channels Informer (LMSC-Informer), which integrates deep learning with reservoir engineering principles. It employs an evaluation method for the development of dominant seepage channels and a label matrix for seepage channels. Unlike existing approaches that primarily depend on geological and formation parameters, this method leverages reservoir production data, making it more accessible and versatile. An experimental prediction conducted in a reservoir in China demonstrated the practical effectiveness of the proposed methods, achieving a prediction accuracy of 73.9%. |
| format | Article |
| id | doaj-art-2d03f6c99bc04d8a9993241f02e7f4da |
| institution | OA Journals |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-2d03f6c99bc04d8a9993241f02e7f4da2025-08-20T02:39:02ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-04-0115511710.1007/s13202-025-01984-yDevelopment of a deep learning-based model for predicting of dominant seepage channels in oil reservoirsChen Liu0Zenghua Zhang1Wensheng Zhou2Chengyu Luo3Lei Jiang4State Key Laboratory of Offshore Oil and Gas Exploitation (abbr. SKLOOGE)State Key Laboratory of Offshore Oil and Gas Exploitation (abbr. SKLOOGE)State Key Laboratory of Offshore Oil and Gas Exploitation (abbr. SKLOOGE)School of Computer Science and Engineering, Hunan University of Science and TechnologySchool of Computer Science and Engineering, Hunan University of Science and TechnologyAbstract The cost of implementing improved oil recovery measures after the formation of dominant seepage channels is high, and the effectiveness is often not significant. By predicting the formation of dominant seepage channels and actively intervening in their early stages, it is possible to reduce development costs and improve oil recovery factor. Consequently, the prediction of dominant seepage channels has emerged as a key focus of research in reservoir engineering. This research introduces a novel method for predicting dominant seepage channels, termed Label Matrix of Seepage Channels Informer (LMSC-Informer), which integrates deep learning with reservoir engineering principles. It employs an evaluation method for the development of dominant seepage channels and a label matrix for seepage channels. Unlike existing approaches that primarily depend on geological and formation parameters, this method leverages reservoir production data, making it more accessible and versatile. An experimental prediction conducted in a reservoir in China demonstrated the practical effectiveness of the proposed methods, achieving a prediction accuracy of 73.9%.https://doi.org/10.1007/s13202-025-01984-yDominant seepage channelsInformerEvaluation of dominant seepage channelsPredicting |
| spellingShingle | Chen Liu Zenghua Zhang Wensheng Zhou Chengyu Luo Lei Jiang Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs Journal of Petroleum Exploration and Production Technology Dominant seepage channels Informer Evaluation of dominant seepage channels Predicting |
| title | Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs |
| title_full | Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs |
| title_fullStr | Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs |
| title_full_unstemmed | Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs |
| title_short | Development of a deep learning-based model for predicting of dominant seepage channels in oil reservoirs |
| title_sort | development of a deep learning based model for predicting of dominant seepage channels in oil reservoirs |
| topic | Dominant seepage channels Informer Evaluation of dominant seepage channels Predicting |
| url | https://doi.org/10.1007/s13202-025-01984-y |
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