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Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

文献类型：期刊论文

作者Rong, Jia1,3,4; Zheng, Zongyuan1,3,4; Luo, Xiaorong1,3,4; Li, Chao3,4; Li, Yuping2; Wei, Xiangfeng2; Wei, Quanchao2; Yu, Guangchun2; Zhang, Likuan3,4; Lei, Yuhong3,4
刊名GEOFLUIDS
出版日期2021-09-26
卷号2021页码:13
ISSN号1468-8115
DOI10.1155/2021/6794213
英文摘要The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Delta LogR method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.
WOS关键词TOTAL ORGANIC-CARBON ; SOURCE ROCKS ; WELL LOGS ; IDENTIFICATION ; MODEL ; RESISTIVITY ; RICHNESS ; POROSITY ; TRENDS ; MATTER
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14010202] ; National Science and Technology Major Project[2017ZX05008-004]
WOS研究方向Geochemistry & Geophysics ; Geology
语种英语
WOS记录号WOS:000703328300003
出版者WILEY-HINDAWI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project ; National Science and Technology Major Project
源URL[http://ir.iggcas.ac.cn/handle/132A11/102729]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Rong, Jia
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.SINOPEC, Explorat Branch Co, Chengdu 610041, Peoples R China
3.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Rong, Jia,Zheng, Zongyuan,Luo, Xiaorong,et al. Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example[J]. GEOFLUIDS,2021,2021:13.
APA Rong, Jia.,Zheng, Zongyuan.,Luo, Xiaorong.,Li, Chao.,Li, Yuping.,...&Lei, Yuhong.(2021).Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example.GEOFLUIDS,2021,13.
MLA Rong, Jia,et al."Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example".GEOFLUIDS 2021(2021):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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