Lithologic Reservoirs ›› 2022, Vol. 34 ›› Issue (1): 130-138.doi: 10.12108/yxyqc.20220113

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

Evaluation of shale TOC content based on two machine learning methods: A case study of Wufeng-Longmaxi Formation in southern Sichuan Basin

YANG Zhanwei1,2, JIANG Zhenxue1,2, LIANG Zhikai1,2, WU Wei3, WANG Junxia1,2, GONG Houjian1,2, LI Weibang1,2, SU Zhanfei1,2, HAO Mianzhu1,2   

  1. 1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China;
    2. Research Institute of Unconventional Oil and Gas Science and Technology, China University of Petroleum(Beijing), Beijing 102249, China;
    3. Shale Gas Research Institute, PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China;
    4. College of Science, China University of Petroleum(Beijing), Beijing 102249, China
  • Received:2021-06-17 Revised:2021-08-02 Published:2022-01-21

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Abstract: In order to establish a reasonable and accurate prediction method of shale total organic carbon(TOC) content of Wufeng-Longmaxi Formation in southern Sichuan Basin,the principal component analysis method was used to preprocess the logging curves and the measured TOC content data of 17 wells in Changning and Luzhou areas. Two TOC content prediction models were established based on BP neural network and gradient boosting decision tree(GBDT),and compared with the traditional TOC content prediction methods. The results show that:(1) The accuracy of the two models is higher than that of the traditional methods,and the consistence between the predicted results and the actual values can meet the requirements.(2) Compared with BP neural network model, GBDT has higher prediction accuracy,and the root mean square error is only 0.0387. The TOC content prediction model established by GBDT has the characteristics of low cost,high efficiency and continuity,and can be used to predict the TOC content of the target layer quickly and accurately. This achievement can provide effective technical support for improving the efficiency of shale oil and gas exploration and development.

Key words: principal component analysis, BP neural network, gradient boosting decision tree, Wufeng-Longmaxi Formation, southern Sichuan Basin

CLC Number: 

  • TE122
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