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Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network

文献类型：期刊论文

作者Shan, Xiaocai4; Chen, Zhangxin1; Fu, Boye3; Zhang, Wang2; Li, Jing1; Wu, Keliu1
刊名GEOPHYSICS
出版日期2023-05-01
卷号88期号:3页码:D193-D206
ISSN号0016-8033
DOI10.1190/GEO2022-0324.1
英文摘要The total organic carbon (TOC) is a key geologic parameter for unconventional reservoirs. Conventional empirical ? Log R methods cannot handle the nonlinear relationships between the characteristics of TOC and its well-log responses. Increased data availability has the potential to speed up deep learning applications, which can reasonably propagate the integrated information from well logs to indirectly observable geologic properties, such as TOC. Although the existing convolutional neural network (CNN) has found superior performance to ? Log R for predicting TOC, CNNs feature-learning capability is still constrained by the fact that it can only extract log-specific sequential features of the input logs. However, the cross-log topological association features are potentially essential for the nonlinear mapping between well logs and TOC. Thus, we introduce a novel deep spatial-sequential graph convolu-tional network (SSGCN) for predicting the TOC by jointly leveraging the cross-log topological association features and log-specific sequential features. Through further use of the previously unaccounted topological interactions, our SSGCN dramatically outperforms the sequence-based CNN. In the southeast Sichuan Basin, SSGCN exhibits beneficial mapping not demonstrated previously: its models achieve a better cross-validation performance within the same gas field wells and a greater generalizability in another gas field well. Our SSGCN method can predict TOC of shale gas field well with the best R-2 being 0.87 within 1 s on the CPU of a desktop com-puter, which increases the efficiency of obtaining the TOC parameter. From this study, we recommend graph and sequential convolutions for designing deep learning architectures in the well-log analysis.
WOS关键词SILURIAN LONGMAXI FORMATION ; SHALE GAS ENRICHMENT ; NEURAL-NETWORK ; SOUTHEAST SICHUAN ; DINGSHAN AREA ; WIRELINE LOGS ; SOURCE ROCKS ; TOC ; MACHINE ; BASIN
资助项目International Partnership Pro-gram of the Chinese Academy of Sciences[GJHZ1776]
WOS研究方向Geochemistry & Geophysics
语种英语
出版者SOC EXPLORATION GEOPHYSICISTS - SEG
WOS记录号WOS:001012385000004
资助机构International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences ; International Partnership Pro-gram of the Chinese Academy of Sciences
源URL[http://ir.iggcas.ac.cn/handle/132A11/111230]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
通讯作者Li, Jing; Wu, Keliu
作者单位1.China Univ Petr, State Key Lab Petr Resources & Prospecting, China, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing, Peoples R China
3.Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, China, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, China, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Shan, Xiaocai,Chen, Zhangxin,Fu, Boye,et al. Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network[J]. GEOPHYSICS,2023,88(3):D193-D206.
APA Shan, Xiaocai,Chen, Zhangxin,Fu, Boye,Zhang, Wang,Li, Jing,&Wu, Keliu.(2023).Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network.GEOPHYSICS,88(3),D193-D206.
MLA Shan, Xiaocai,et al."Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network".GEOPHYSICS 88.3(2023):D193-D206.

入库方式： OAI收割

来源：地质与地球物理研究所

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