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Sustainability
Volume 17
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10.3390/su17010266
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Article

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

by
Fengyu Li
1,2,3,
Xia Guo
4,
Xiaofei Qi
5,
Bo Feng
1,
Jie Liu
6,*,
Yunpeng Xie
6 and
Yumeng Gu
6
1
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
2
Huzhou Vocational and Technical College, Huzhou 313000, China
3
Key Laboratory of Deep Geothermal Resources, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China
4
Nuclear Industry Huzhou Survey Planning Design and Research Institute Co., Ltd., Huzhou 313000, China
5
No. 2 Exploration Team, Hebei Bureau of Coal Geological Exploration, Xingtai 054000, China
6
College of Hydrology & Water Resources, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(1), 266; https://doi.org/10.3390/su17010266
Submission received: 2 December 2024 / Revised: 28 December 2024 / Accepted: 30 December 2024 / Published: 2 January 2025
(This article belongs to the Topic Clean and Low Carbon Energy, 2nd Edition)
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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 well-doublet placement optimization; hydro-thermal coupling model; LSTM-CNN model; sustainable energy; grey wolf optimizer

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MDPI and ACS Style

Li, F.; Guo, X.; Qi, X.; Feng, B.; Liu, J.; Xie, Y.; Gu, Y. A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer. Sustainability 2025, 17, 266. https://doi.org/10.3390/su17010266

AMA Style

Li F, Guo X, Qi X, Feng B, Liu J, Xie Y, Gu Y. A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer. Sustainability. 2025; 17(1):266. https://doi.org/10.3390/su17010266

Chicago/Turabian Style

Li, Fengyu, Xia Guo, Xiaofei Qi, Bo Feng, Jie Liu, Yunpeng Xie, and 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 17, no. 1: 266. https://doi.org/10.3390/su17010266

APA Style

Li, F., Guo, X., Qi, X., Feng, B., Liu, J., Xie, Y., & Gu, Y. (2025). A Surrogate Model-Based Optimization Approach for Geothermal Well-Doublet Placement Using a Regularized LSTM-CNN Model and Grey Wolf Optimizer. Sustainability, 17(1), 266. https://doi.org/10.3390/su17010266

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