Yufang Xue , Bing Luo , Yalin Chen , Jun Qin , Lanpu Chen , Yuhao Yi
{"title":"基于深度学习方法的海相页岩岩相分类","authors":"Yufang Xue , Bing Luo , Yalin Chen , Jun Qin , Lanpu Chen , Yuhao Yi","doi":"10.1016/j.jappgeo.2025.105902","DOIUrl":null,"url":null,"abstract":"<div><div>Shale lithofacies control hydrocarbon generation potential, reservoir properties, and anisotropy, thereby determining the distribution of shale “sweet spots” and guiding the selection of horizontal well target windows. Consequently, predicting shale lithofacies is crucial for the detailed evaluation, efficient exploration, and development of shale reservoirs. However, predicting lithofacies within the Wujiaping Formation of the Permian in the Hongxing area, eastern Sichuan Basin, presents significant challenges due to complex mineral compositions and strong heterogeneity. In this study, we classified the shale and its interlayers within the reservoir into five lithofacies based on total organic carbon content and mineral assemblages, with MF4 identified as the favorable lithofacies. Additionally, six logging curves (GR, AC, DEN, CNL, CAL, and LLD) were selected as input features to train and test deep learning models for automatic lithofacies prediction. To investigate the applicability of deep learning models for lithofacies identifications using well logs, six models were employed, including CNN, CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, and Transformer. Notably, the Transformer model outperformed the others, achieving an <span><math><mi>Accuracy</mi></math></span> of 0.8776 and <span><math><mi>Precision</mi></math></span> of 0.9152 in shale lithofacies identification. Specifically, for the favorable lithofacies MF4, the Transformer model yielded the highest prediction performance, with <span><math><mi>F</mi><mn>1</mn><mo>−</mo><mi>score</mi></math></span> of 0.89. This study demonstrates that deep learning models can effectively identify the shale lithofacies from conventional well logs, providing valuable technical insights for developing practical approaches to identify lithofacies.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105902"},"PeriodicalIF":2.1000,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven lithofacies classification of marine shale based on deep learning approaches\",\"authors\":\"Yufang Xue , Bing Luo , Yalin Chen , Jun Qin , Lanpu Chen , Yuhao Yi\",\"doi\":\"10.1016/j.jappgeo.2025.105902\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Shale lithofacies control hydrocarbon generation potential, reservoir properties, and anisotropy, thereby determining the distribution of shale “sweet spots” and guiding the selection of horizontal well target windows. Consequently, predicting shale lithofacies is crucial for the detailed evaluation, efficient exploration, and development of shale reservoirs. However, predicting lithofacies within the Wujiaping Formation of the Permian in the Hongxing area, eastern Sichuan Basin, presents significant challenges due to complex mineral compositions and strong heterogeneity. In this study, we classified the shale and its interlayers within the reservoir into five lithofacies based on total organic carbon content and mineral assemblages, with MF4 identified as the favorable lithofacies. Additionally, six logging curves (GR, AC, DEN, CNL, CAL, and LLD) were selected as input features to train and test deep learning models for automatic lithofacies prediction. To investigate the applicability of deep learning models for lithofacies identifications using well logs, six models were employed, including CNN, CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, and Transformer. Notably, the Transformer model outperformed the others, achieving an <span><math><mi>Accuracy</mi></math></span> of 0.8776 and <span><math><mi>Precision</mi></math></span> of 0.9152 in shale lithofacies identification. Specifically, for the favorable lithofacies MF4, the Transformer model yielded the highest prediction performance, with <span><math><mi>F</mi><mn>1</mn><mo>−</mo><mi>score</mi></math></span> of 0.89. This study demonstrates that deep learning models can effectively identify the shale lithofacies from conventional well logs, providing valuable technical insights for developing practical approaches to identify lithofacies.</div></div>\",\"PeriodicalId\":54882,\"journal\":{\"name\":\"Journal of Applied Geophysics\",\"volume\":\"242 \",\"pages\":\"Article 105902\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Geophysics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926985125002836\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985125002836","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Data-driven lithofacies classification of marine shale based on deep learning approaches
Shale lithofacies control hydrocarbon generation potential, reservoir properties, and anisotropy, thereby determining the distribution of shale “sweet spots” and guiding the selection of horizontal well target windows. Consequently, predicting shale lithofacies is crucial for the detailed evaluation, efficient exploration, and development of shale reservoirs. However, predicting lithofacies within the Wujiaping Formation of the Permian in the Hongxing area, eastern Sichuan Basin, presents significant challenges due to complex mineral compositions and strong heterogeneity. In this study, we classified the shale and its interlayers within the reservoir into five lithofacies based on total organic carbon content and mineral assemblages, with MF4 identified as the favorable lithofacies. Additionally, six logging curves (GR, AC, DEN, CNL, CAL, and LLD) were selected as input features to train and test deep learning models for automatic lithofacies prediction. To investigate the applicability of deep learning models for lithofacies identifications using well logs, six models were employed, including CNN, CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, and Transformer. Notably, the Transformer model outperformed the others, achieving an of 0.8776 and of 0.9152 in shale lithofacies identification. Specifically, for the favorable lithofacies MF4, the Transformer model yielded the highest prediction performance, with of 0.89. This study demonstrates that deep learning models can effectively identify the shale lithofacies from conventional well logs, providing valuable technical insights for developing practical approaches to identify lithofacies.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.