Research and Application of Complex Lithology Identification Method Based on CNN-GRU
In the exploration and development of oil and gas reservoirs, lithology identification is an important component of reservoir log evaluation. The Hailar basin is characterized by proximal sedimentation and transport deposition, with significant differences in rock composition and structure. The type...
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Editorial Office of Well Logging Technology
2023-12-01
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| Series: | Cejing jishu |
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| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5539 |
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| author | ZHANG Xiaofeng PANG Chunyang HU Rui ZHU Yunfeng LI Hongxing |
| author_facet | ZHANG Xiaofeng PANG Chunyang HU Rui ZHU Yunfeng LI Hongxing |
| author_sort | ZHANG Xiaofeng |
| collection | DOAJ |
| description | In the exploration and development of oil and gas reservoirs, lithology identification is an important component of reservoir log evaluation. The Hailar basin is characterized by proximal sedimentation and transport deposition, with significant differences in rock composition and structure. The types of lithology are complex and diverse, mainly including fine sandy mudstone, siltstone, shale, tuffaceous sandstone, tuffaceous mudstone, oil-bearing coarse sandstone, oil-bearing fine sandstone, crystal detrital tuff conglomerate, sandstone conglomerate, and dense tuff conglomerate. Traditional identification methods have low accuracy in dealing with complex lithologies, which severely restricts the accuracy of reservoir logging interpretation. This study integrates convolutional neural networks with gated recurrent units (CNN-GRU) and selects six logging parameters, including sonic time difference, natural potential, natural gamma, density, and shallow and deep lateral resistivity, to train sample wells in the Hailar basin. A CNN-GRU model for identifying complex lithologies is constructed. The research results show that the average accuracy of the CNN-GRU model reaches 92.3%, with an improvement of 5.5%～10.0% compared to a single network. After applying this model to well A in the Hailar basin, the lithology identification conformity rate reaches 94.8%, which provides a reliable lithological basis for the accuracy of reservoir log interpretation. |
| format | Article |
| id | doaj-art-391187a932ff4e5cac4f20f1b1d7fe0c |
| institution | DOAJ |
| issn | 1004-1338 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Well Logging Technology |
| record_format | Article |
| series | Cejing jishu |
| spelling | doaj-art-391187a932ff4e5cac4f20f1b1d7fe0c2025-08-20T03:08:18ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382023-12-0147666267010.16489/j.issn.1004-1338.2023.06.0031004-1338(2023)06-0662-09Research and Application of Complex Lithology Identification Method Based on CNN-GRUZHANG Xiaofeng0PANG Chunyang1HU Rui2ZHU Yunfeng3LI Hongxing4School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, ChinaSchool of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, ChinaSchool of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, ChinaSchool of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, ChinaSchool of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, ChinaIn the exploration and development of oil and gas reservoirs, lithology identification is an important component of reservoir log evaluation. The Hailar basin is characterized by proximal sedimentation and transport deposition, with significant differences in rock composition and structure. The types of lithology are complex and diverse, mainly including fine sandy mudstone, siltstone, shale, tuffaceous sandstone, tuffaceous mudstone, oil-bearing coarse sandstone, oil-bearing fine sandstone, crystal detrital tuff conglomerate, sandstone conglomerate, and dense tuff conglomerate. Traditional identification methods have low accuracy in dealing with complex lithologies, which severely restricts the accuracy of reservoir logging interpretation. This study integrates convolutional neural networks with gated recurrent units (CNN-GRU) and selects six logging parameters, including sonic time difference, natural potential, natural gamma, density, and shallow and deep lateral resistivity, to train sample wells in the Hailar basin. A CNN-GRU model for identifying complex lithologies is constructed. The research results show that the average accuracy of the CNN-GRU model reaches 92.3%, with an improvement of 5.5%～10.0% compared to a single network. After applying this model to well A in the Hailar basin, the lithology identification conformity rate reaches 94.8%, which provides a reliable lithological basis for the accuracy of reservoir log interpretation.https://www.cnpcwlt.com/#/digest?ArticleID=5539complex lithology identificationhailar basinconvolutional neural networkgated recurrent unitlong short-term memory neural network |
| spellingShingle | ZHANG Xiaofeng PANG Chunyang HU Rui ZHU Yunfeng LI Hongxing Research and Application of Complex Lithology Identification Method Based on CNN-GRU Cejing jishu complex lithology identification hailar basin convolutional neural network gated recurrent unit long short-term memory neural network |
| title | Research and Application of Complex Lithology Identification Method Based on CNN-GRU |
| title_full | Research and Application of Complex Lithology Identification Method Based on CNN-GRU |
| title_fullStr | Research and Application of Complex Lithology Identification Method Based on CNN-GRU |
| title_full_unstemmed | Research and Application of Complex Lithology Identification Method Based on CNN-GRU |
| title_short | Research and Application of Complex Lithology Identification Method Based on CNN-GRU |
| title_sort | research and application of complex lithology identification method based on cnn gru |
| topic | complex lithology identification hailar basin convolutional neural network gated recurrent unit long short-term memory neural network |
| url | https://www.cnpcwlt.com/#/digest?ArticleID=5539 |
| work_keys_str_mv | AT zhangxiaofeng researchandapplicationofcomplexlithologyidentificationmethodbasedoncnngru AT pangchunyang researchandapplicationofcomplexlithologyidentificationmethodbasedoncnngru AT hurui researchandapplicationofcomplexlithologyidentificationmethodbasedoncnngru AT zhuyunfeng researchandapplicationofcomplexlithologyidentificationmethodbasedoncnngru AT lihongxing researchandapplicationofcomplexlithologyidentificationmethodbasedoncnngru |