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Development of visual prediction model for shale gas wells production based on screening main controlling factors

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  • Niu, Wente
  • Lu, Jialiang
  • Sun, Yuping
  • Guo, Wei
  • Liu, Yuyang
  • Mu, Ying

Abstract

For shale gas development, clarification of the main controlling factors of production and estimated ultimate recovery (EUR) with high accuracy is indispensable. The selection of 16 critical parameters directed toward the visual output of the objective function were the most influential factors determined through a sensitivity analysis. Based on the fundamental parameters, the distance correlation coefficient was used to clarify the main controlling factors affecting the EUR of shale gas wells in Weiyuan block. Then, visual forecasting models of EUR were established using Response Surface Method (RSM), Multi-layer Feedforward Neural Network (MLFNN) and Least Square Support Vector Machine (LSSVM). Furthermore, the models developed by the three methods are compared and analyzed. The field application results of the model indicated that the model based on the LSSVM has the best field application effect. The proposed model is a serviceable tool for EUR prediction. In addition, the use of the model is efficient and convenient, and only six main controlling factors can be used to achieve the prediction of EUR. The results of this study can be extended as the main controlling factors analysis and the development of EUR visual model of shale gas wells in other blocks.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007150
    DOI: 10.1016/j.energy.2022.123812
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    References listed on IDEAS

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    1. 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.
    2. Chen, Shangbin & Zhu, Yanming & Wang, Hongyan & Liu, Honglin & Wei, Wei & Fang, Junhua, 2011. "Shale gas reservoir characterisation: A typical case in the southern Sichuan Basin of China," Energy, Elsevier, vol. 36(11), pages 6609-6616.
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    4. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
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    Cited by:

    1. 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).
    2. Niu, Wente & Lu, Jialiang & Sun, Yuping & Zhang, Xiaowei & Li, Qiaojing & Cao, Xu & Liang, Pingping & Zhan, Hongming, 2024. "Techno-economic integration evaluation in shale gas development based on ensemble learning," Applied Energy, Elsevier, vol. 357(C).
    3. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    4. Kasala, Erasto E. & Wang, Jinjie & Lwazi, Hussein M. & Nyakilla, Edwin E. & Kibonye, John S., 2024. "The influence of hydraulic fracture and reservoir parameters on the storage of CO2 and enhancing CH4 recovery in Yanchang formation," Energy, Elsevier, vol. 296(C).
    5. Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).

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