Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs
Abstract A Sequence-Variable Attention Temporal Convolutional Network (SVA-TCN) is proposed for lithology classification based on well log data. This study aims to address the issue that native TCN pays insufficient attention to crucial logging variables and sequence structural features in well log...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-024-01887-4 |
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| _version_ | 1823863264456474624 |
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| author | Hanlin Feng Zitong Zhang Chunlei Zhang Chengcheng Zhong Qiaoyu Ma |
| author_facet | Hanlin Feng Zitong Zhang Chunlei Zhang Chengcheng Zhong Qiaoyu Ma |
| author_sort | Hanlin Feng |
| collection | DOAJ |
| description | Abstract A Sequence-Variable Attention Temporal Convolutional Network (SVA-TCN) is proposed for lithology classification based on well log data. This study aims to address the issue that native TCN pays insufficient attention to crucial logging variables and sequence structural features in well log tasks. A novel Sequence-Variable Attention Mechanism module is designed to effectively extract features by adding the bidirectional attention mechanism, which comprises the sequence attention mechanism and the variable attention mechanism. The sequence attention mechanism, acting along the depth dimension, focuses on modeling the multiscale sequence features. The variable attention mechanism, working along the logging variable dimension, contributes to learning the importance of different logging variables and exploring the internal correlations among them. To verify the validity of the model, experiments are conducted in the volcanic reservoir of the Yingcheng Formation in the Xujiaweizi Depression of the Songliao Basin. Seven logging variables sensitive to lithological changes are selected, including compressional wave slowness, density, and compensated neutron logging, etc., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. The findings of this study indicate that the SVA-TCN model is well-suited for lithology identification in volcanic reservoirs. It provides more reliable prediction results and exhibits good stability and generalization, offering a new avenue to volcanic lithology identification. |
| format | Article |
| id | doaj-art-45ed8975c2d14b2f9a78195785fb7bdc |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-45ed8975c2d14b2f9a78195785fb7bdc2025-02-09T12:13:25ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-01-0115111510.1007/s13202-024-01887-4Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logsHanlin Feng0Zitong Zhang1Chunlei Zhang2Chengcheng Zhong3Qiaoyu Ma4School of Computer and Information Technology, Northeast Petroleum UniversityState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)Beijing Zhongdi Runde Petroleum Technology Co.,Ltd.College of Information and Electrical Engineering, China Agricultural UniversityCollege of Information and Electrical Engineering, China Agricultural UniversityAbstract A Sequence-Variable Attention Temporal Convolutional Network (SVA-TCN) is proposed for lithology classification based on well log data. This study aims to address the issue that native TCN pays insufficient attention to crucial logging variables and sequence structural features in well log tasks. A novel Sequence-Variable Attention Mechanism module is designed to effectively extract features by adding the bidirectional attention mechanism, which comprises the sequence attention mechanism and the variable attention mechanism. The sequence attention mechanism, acting along the depth dimension, focuses on modeling the multiscale sequence features. The variable attention mechanism, working along the logging variable dimension, contributes to learning the importance of different logging variables and exploring the internal correlations among them. To verify the validity of the model, experiments are conducted in the volcanic reservoir of the Yingcheng Formation in the Xujiaweizi Depression of the Songliao Basin. Seven logging variables sensitive to lithological changes are selected, including compressional wave slowness, density, and compensated neutron logging, etc., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. The findings of this study indicate that the SVA-TCN model is well-suited for lithology identification in volcanic reservoirs. It provides more reliable prediction results and exhibits good stability and generalization, offering a new avenue to volcanic lithology identification.https://doi.org/10.1007/s13202-024-01887-4Lithology identificationTemporal convolutional networkAttention mechanismVolcanic reservoirWell logs |
| spellingShingle | Hanlin Feng Zitong Zhang Chunlei Zhang Chengcheng Zhong Qiaoyu Ma Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs Journal of Petroleum Exploration and Production Technology Lithology identification Temporal convolutional network Attention mechanism Volcanic reservoir Well logs |
| title | Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| title_full | Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| title_fullStr | Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| title_full_unstemmed | Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| title_short | Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| title_sort | sequence variable attention temporal convolutional network for volcanic lithology identification based on well logs |
| topic | Lithology identification Temporal convolutional network Attention mechanism Volcanic reservoir Well logs |
| url | https://doi.org/10.1007/s13202-024-01887-4 |
| work_keys_str_mv | AT hanlinfeng sequencevariableattentiontemporalconvolutionalnetworkforvolcaniclithologyidentificationbasedonwelllogs AT zitongzhang sequencevariableattentiontemporalconvolutionalnetworkforvolcaniclithologyidentificationbasedonwelllogs AT chunleizhang sequencevariableattentiontemporalconvolutionalnetworkforvolcaniclithologyidentificationbasedonwelllogs AT chengchengzhong sequencevariableattentiontemporalconvolutionalnetworkforvolcaniclithologyidentificationbasedonwelllogs AT qiaoyuma sequencevariableattentiontemporalconvolutionalnetworkforvolcaniclithologyidentificationbasedonwelllogs |