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.

The well-doublet production model has far-reaching implications for the sustainable utilization of geothermal resources. The position of the injection well in the geothermal production process is closely connected to the emergence of thermal breakthroughs and the production lifespan. Thus, it is necessary to optimize the well placement. However, traditional simulation and optimization approaches require a long time and have a high computing burden. In this paper, a surrogate model based on the back propagation neural network (BPNN) is trained to improve the drawbacks of previous approaches, and it is combined with the genetic algorithm (GA) to develop a simulation-optimization approach to find the optimal well placement of a well-doublet geothermal production system. To guarantee that the training data have appropriate physical significance, the TOUGH2 program is used for the hydro–thermal model development of the geothermal reservoir of the Minghuazhen Formation in Tianjin, China. A sensitivity analysis is used to select the series of samples used for training, which includes temperature and pressure variation, heat extraction rate (Wh), and economic cost. The results reveal that the surrogate model has excellent prediction accuracy and efficiency for physical processes, and the genetic algorithm optimization outcomes are consistent with predictions, which is of practical importance.

Understanding how catchments respond to environmental changes is critical for water resource management. However, few studies have systematically linked catchment characteristics, environmental changes, and hydrological responses. Therefore, this study proposes a novel dual‐clustering approach for identifying hydrological response patterns. It constructs the catchment characteristic indicator system for the baseline period and introduces dynamic similarity indicators that reflect climate change and anthropogenic impacts to achieve dual clustering, thereby identifying hydrological response differences. Furthermore, it employs the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) methods to identify key influencing factors of runoff change significance, providing interpretable insights into differences in hydrological responses. The approach is applied to the Haihe River Basin, indicating that 160 catchments are classified into nine static groups (A1–A9) based on catchment characteristics and five dynamic groups (B1–B5) based on environmental changes. The XGBoost model demonstrates good performance in identifying hydrological response patterns, SHAP analysis identifies the top four important factors as percentage of areas with substantial declines in the water table (positive), proportion of natural land use (positive), degree of humidity (negative), and mean elevation (positive). Catchments located in the northwestern mountainous areas are more susceptible to environmental changes, while those located in the southwestern mountainous areas and the southern plains show relatively stable response patterns. Additionally, environmental change patterns characterized by substantial water table decline are more likely to trigger significant runoff change. This approach provides new insights into the effects of interactions between static catchment characteristics and dynamic environmental changes on hydrological functioning.