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

作者：Wenjie Qiu, Dianguang Liu, Yun Yang, Jian Song, Weimin Que, Zhengbang LIU, Haicheng Weng, Jianfeng Wu, Jianfeng Wu, Jichun Wu, Jichun Wu · 发表于：Water Resources Research · 年份：2026 · DOI：10.1029/2025wr041741 · 被引用次数：1 · 研究领域：Radioactive element chemistry and processing、Groundwater flow and contamination studies、CO2 Sequestration and Geologic Interactions

Abstract 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 objectiv...