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  2. 地质与地球物理研究所
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  4. 岩石圈演化国家重点实验室
A deep encoder-decoder neural network model for total organic carbon content prediction from well logs

文献类型：期刊论文

作者Zhang, Wang1,2; Shan, Xiaocai2; Fu, Boye3; Zou, Xinyu4; Fu, Li-Yun5
刊名JOURNAL OF ASIAN EARTH SCIENCES
出版日期2022-12-01
卷号240页码:13
关键词Total organic carbon (TOC) Well logs Deep Encoder-decoder Neural Network Multi-scale feature fusion Saliency
ISSN号1367-9120
DOI10.1016/j.jseaes.2022.105437
英文摘要Total organic carbon (TOC) content is an important geochemical parameter for evaluating the hydrocarbon generation potential of unconventional oil and gas resources. The TOC content of source rocks significantly affects the responses of well logs so, in principle, well logs can be used for source rock appraisal. However, the complex relationships between TOC content and well logs involve nonlinear mapping with many parameters. It is sometimes difficult to obtain continuous and accurate TOC content values using conventional methods such as the ALogR method. In this study, we propose a TOC prediction model using a deep encoder-decoder neural network (DEDNN) based on mining and mapping of multiscale features of logging curves. The prediction per-formance of the model is validated by a series of tests using data from four exploration wells in the Longmaxi black shale in the Dingshan area of the Sichuan Basin. The TOC content prediction results confirm that the proposed DEDNN is more accurate than either the ALogR method or CNN, which is a state-of-the-art convolu-tional neural network. Furthermore, a saliency map derived from the DEDNN results shows the relative importance of different well logs to TOC contents.
WOS关键词LONGMAXI FORMATION ; SOURCE ROCKS ; TOC PREDICTION ; DINGSHAN AREA ; WIRELINE LOGS ; U-NET ; SHALE ; MACHINE ; BASIN ; IDENTIFICATION
资助项目International Partnership Program of the Chinese Academy of Sciences ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; [GJHZ1776] ; [33550000-22-ZC0613-0006]
WOS研究方向Geology
语种英语
WOS记录号WOS:000882798100002
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; International Partnership Program of the Chinese Academy of Sciences ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development ; State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development
源URL[http://ir.iggcas.ac.cn/handle/132A11/107720]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
地质与地球物理研究所_中国科学院矿产资源研究重点实验室
通讯作者Shan, Xiaocai
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing 100029, Peoples R China
3.Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China
5.China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Wang,Shan, Xiaocai,Fu, Boye,et al. A deep encoder-decoder neural network model for total organic carbon content prediction from well logs[J]. JOURNAL OF ASIAN EARTH SCIENCES,2022,240:13.
APA Zhang, Wang,Shan, Xiaocai,Fu, Boye,Zou, Xinyu,&Fu, Li-Yun.(2022).A deep encoder-decoder neural network model for total organic carbon content prediction from well logs.JOURNAL OF ASIAN EARTH SCIENCES,240,13.
MLA Zhang, Wang,et al."A deep encoder-decoder neural network model for total organic carbon content prediction from well logs".JOURNAL OF ASIAN EARTH SCIENCES 240(2022):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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