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Volume 37 Issue 3
Jun 2026
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Article Contents
Journal of Earth Science  > 2026  >  37(3): 985-1006.
Wenqiang Tang, Chao Ma, Shengjian Zhou, Shaomin Zhang, Qiyu Wang, Kunyu Wu, Haitao Hong, Jiashan Lin, Yun Yang, Kai Yu. Key Parameters Prediction of Shale Reservoir Based on Deep-Learning Model: A Case Study of Jurassic Da'anzhai Member in Sichuan Basin. Journal of Earth Science, 2026, 37(3): 985-1006. doi: 10.1007/s12583-025-0329-8
Citation: Wenqiang Tang, Chao Ma, Shengjian Zhou, Shaomin Zhang, Qiyu Wang, Kunyu Wu, Haitao Hong, Jiashan Lin, Yun Yang, Kai Yu. Key Parameters Prediction of Shale Reservoir Based on Deep-Learning Model: A Case Study of Jurassic Da'anzhai Member in Sichuan Basin. Journal of Earth Science, 2026, 37(3): 985-1006. doi: 10.1007/s12583-025-0329-8
shu

Key Parameters Prediction of Shale Reservoir Based on Deep-Learning Model: A Case Study of Jurassic Da'anzhai Member in Sichuan Basin

doi: 10.1007/s12583-025-0329-8
  • 1.

    State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Institute of Sedimentary Geology, College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China

  • 2.

    Key Laboratory of Sedimentary Basin and Oil and Gas Resources, Ministry of Natural Resources, Chengdu 610218, China

  • 3.

    Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications, Ministry of Natural Resources, Chengdu 610059, China

  • 4.

    Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China

  • 5.

    Shandong Jinzhou Mine Industry Group Co., Ltd., Weihai 264500, China

  • 6.

    PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China

  • 7.

    Plateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, PetroChina Qinghai Oilfield Company, Dunhuang 736202, China

More Information
  • Corresponding author: Chao Ma, machao@cdut.edu.cn
  • Received Date: 31 Mar 2025
  • Accepted Date: 02 Jul 2025
  • Issue Publish Date: 30 Jun 2026
    Abstract

  • As an essential unconventional oil and gas resource, shale oil is of great significance to energy replacement and socio-economic development. Total organic carbon (TOC) and pyrolyzed hydrocarbon (S1), as key parameters for hydrocarbon reservoir evaluation, are important guides for practical exploration. Usually, the high precision determination of TOC and S1 requires sample collection and laboratory analysis, but is often compromised due to the cost and the limitation of coring continuity. With the advent of the digital age, increasingly intelligent methods are being employed in this field, such as Δlog R, support vector regression (SVR), and backpropagation neural network (BPNN). However, Δlog R has low performance, SVR does wrong in feature extraction, and BPNN is prone to local optimum. The coefficients of determination (R2) for TOC prediction using the three methods registered values of 0.25, 0.69, and 0.74. In contrast, the R2 values for S1 prediction were 0.23, 0.54, and 0.58. Thus, a low-cost, intelligent, and high-precision method to predict TOC and S1 is needed. This paper proposes a new model for predicting TOC and S1 in shale reservoirs based on an improved Deep learning network model (Encoder-ECA) based on the Transformer. With nearly 3 000 rock samples selected from the Jurassic Da'anzhai Member of the Sichuan Basin, China, and sedimentary facies variations in the study area, our results show that the Encoder-ECA model achieves an R2 of 0.86 for TOC content prediction and an R2 of 0.82 for S1 content prediction. In addition, the Encoder-ECA model was successfully applied to the recently implemented exploratory well evaluations in the study area, and the prediction results were used to optimize the sweet spot section, with a combined daily production of 22.4 mcf of oil and 33 600 mcf of gas. Simultaneously, data from different basins will be utilized to validate the applicability range of the model. This research demonstrates the great potential of deep learning technology in unconventional resource evaluation. It confirms the application of the Encoder-ECA model in the exploration practice of lacustrine facies shale oil and gas.

     

    • Conflict of Interest
    The authors declare that they have no conflict of interest.
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