Integrating Deep Learning and Distance‐Based Clustering to Optimize the Field Scale In Situ Uranium Leaching System in Heterogeneous Reservoirs

Utilization of the integrated simulation‐optimization models for supporting decisions of the in situ leaching (ISL) design of uranium (U) mining is often hampered by the physicochemical heterogeneity within the sandstone reservoirs. Nevertheless, the conventional way suffers from a high conceptual uncertainty due to almost ubiquitous simplifying assumptions used in model parameterizations. Additionally, the increasing complexity of process‐based reactive transport simulators results in substantial computational demands, limiting the feasibility of conducting numerous model evaluations. Addressing the optimization challenges posed by geological uncertainty typically involves Monte Carlo‐based population search methods with evolutionary algorithms which are often computationally intensive and suffer from excessive model redundancy. This study presents a novel optimization framework for identifying the optimal well control strategies for a field‐scale neutral ISL of U mining system in the Songliao Basin, China. The proposed approach integrates a deep learning‐based proxy model with distance‐based clustering components. Specifically, a ResNet‐LSTM network is employed to predict dynamic U recovery concentration. A small subset of representative reservoir realizations is selected through clustering analysis, effectively capturing the uncertainty space without relying on the full ensemble. The subset is then embedded into a heuristic evolutionary algorithm with the objective of maximizing economic benefits. The results demonstrate that this integrated framework significantly enhances the decision‐making process in a computationally efficient way. By integrating the proxy model with cluster‐based realization selection, the proposed procedure achieves a 15.2% improvement in net present value compared to unoptimized scenarios. Overall, the framework provides a versatile and powerful tool for robust optimization in heterogeneous reservoirs.