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A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer

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  • Fengyu Li

    (Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
    Huzhou Vocational and Technical College, Huzhou 313000, China
    Key Laboratory of Deep Geothermal Resources, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China)

  • Xia Guo

    (Nuclear Industry Huzhou Survey Planning Design and Research Institute Co., Ltd., Huzhou 313000, China)

  • Xiaofei Qi

    (No. 2 Exploration Team, Hebei Bureau of Coal Geological Exploration, Xingtai 054000, China)

  • Bo Feng

    (Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China)

  • Jie Liu

    (College of Hydrology & Water Resources, Hohai University, Nanjing 210098, China)

  • Yunpeng Xie

    (College of Hydrology & Water Resources, Hohai University, Nanjing 210098, China)

  • Yumeng Gu

    (College of Hydrology & Water Resources, Hohai University, Nanjing 210098, China)

Abstract

The placement of a well doublet plays a significant role in geothermal resource sustainable production. The normal well placement optimization method of numerical simulation-based faces a higher computational load with the increasing precision demand. This study proposes a surrogate model-based optimization approach that searches the economically optimal injection well location using the Grey Wolf Optimizer (GWO). The surrogate models trained by the novel Multi-layer Regularized Long Short-Term Memory–Convolution Neural Network concatenation model (MR LSTM-CNN) will relieve the computation load and save the simulation time during the simulation–optimization process. The results showed that surrogate models in a homogenous reservoir and heterogenous reservoir can predict the pressure–temperature evolution time series with the accuracy of 99.80% and 94.03%. Additionally, the optimization result fitted the real economic cost distribution in both reservoir situations. Further comparison figured out that the regularization and convolution process help the Long Short-Term Memory neural network (LSTM) perform better overall than random forest. And GWO owned faster search speed and higher optimization quality than a widely used Genetic Algorithm (GA). The surrogate model-based approach shows the good performance of MR LSTM-CNN and the feasibility in the well placement optimization of GWO, which provides a reliable reference for future study and engineering practice.

Suggested Citation

  • Fengyu Li & Xia Guo & Xiaofei Qi & Bo Feng & Jie Liu & Yunpeng Xie & Yumeng Gu, 2025. "A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer," Sustainability, MDPI, vol. 17(1), pages 1-31, January.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:266-:d:1558804
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    References listed on IDEAS

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