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

Fengyu Li, Xia Guo, Xiaofei Qi, Bo Feng, Jie Liu (), Yunpeng Xie and Yumeng Gu
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Fengyu Li: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, 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

Sustainability, 2025, vol. 17, issue 1, 1-31

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.

Keywords: well-doublet placement optimization; hydro-thermal coupling model; LSTM-CNN model; sustainable energy; grey wolf optimizer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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