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A Production Prediction Method for Shale Gas Wells Based on Multiple Regression

Author

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  • Wente Niu

    (School of Engineering Science, University of Chinese Academy of Sciences, Beijing 101400, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065000, China
    Research Institute of Petroleum Exploration and Development, Beijing 100089, China)

  • Jialiang Lu

    (School of Engineering Science, University of Chinese Academy of Sciences, Beijing 101400, China
    Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Langfang 065000, China
    Research Institute of Petroleum Exploration and Development, Beijing 100089, China)

  • Yuping Sun

    (Research Institute of Petroleum Exploration and Development, Beijing 100089, China)

Abstract

The estimated ultimate recovery (EUR) of a single shale gas well is one of the important evaluation indicators for the scale and benefit development of shale gas, which is affected by many factors such as geological and engineering, so its accurate prediction is difficult. In order to realize the accurate prediction of ultimate recovery, this study considered 172 shale gas wells in the Weiyuan block as samples and selected 19 geological and engineering factors that affect the ultimate recovery of shale gas wells. Furthermore, eight key controlling factors were selected by means of the Pearson correlation coefficient and maximum mutual information coefficient comprehensive evaluation method. The data were divided into training and testing samples. Different numbers of training samples were selected and seven schemes were designed. Based on the key controlling factors, the ultimate recovery prediction model for shale gas wells in this block was established through multiple regression methods. The effectiveness of the prediction model was verified by analyzing the testing samples. The result shows that with the increase of the size of training samples, the error of the ultimate recovery predicted by the model gradually decreases gradually. When predicting the single gas well, the average absolute error of ultimate recovery is less than 20% if the number of the training gas well is more than 80. When analyzing the development potential of similar blocks without drilling, the error of the sum of ultimate recovery is less than 10% if the size of the training gas well reaches 60.

Suggested Citation

  • Wente Niu & Jialiang Lu & Yuping Sun, 2021. "A Production Prediction Method for Shale Gas Wells Based on Multiple Regression," Energies, MDPI, vol. 14(5), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1461-:d:512415
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    References listed on IDEAS

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    1. Wang, Ke & Li, Haitao & Wang, Junchao & Jiang, Beibei & Bu, Chengzhong & Zhang, Qing & Luo, Wei, 2017. "Predicting production and estimated ultimate recoveries for shale gas wells: A new methodology approach," Applied Energy, Elsevier, vol. 206(C), pages 1416-1431.
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    Cited by:

    1. Niu, Wente & Lu, Jialiang & Sun, Yuping & Guo, Wei & Liu, Yuyang & Mu, Ying, 2022. "Development of visual prediction model for shale gas wells production based on screening main controlling factors," Energy, Elsevier, vol. 250(C).
    2. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).
    3. Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).
    4. Lixia Kang & Wei Guo & Xiaowei Zhang & Yuyang Liu & Zhaoyuan Shao, 2022. "Differentiation and Prediction of Shale Gas Production in Horizontal Wells: A Case Study of the Weiyuan Shale Gas Field, China," Energies, MDPI, vol. 15(17), pages 1-13, August.

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