Multi-physics Collaborative Optimization of UGS Gas Injection Based on MOGWO Algorithm

The gas injection scheduling scheme of underground gas storage (UGS) directly affects its safety and operating efficiency. However, existing studies often ignore the hydraulic permeability characteristics of heterogeneous reservoirs and fail to effectively couple the underground rock physics laws with the ground gas injection system. To address this problem, this study proposes an intelligent optimization method based on multi-physics collaborative simulation: firstly, a multi-physics coupling model is constructed by combining the geological characteristics of the UGS reservoir and the Darcy flow law of gas-water two-phases; secondly, a multi-objective grey wolf optimization algorithm (MOGWO) is developed to dynamically optimize the gas injection flow rate. The objective function covers both underground pressure balance and ground system efficiency. Constraints include storage capacity, well pressure and flow restrictions. For the first time, a hyperplane projection algorithm is used to solve complex equality constraints in multi-objective optimization. The experimental results show that: (1) compared with the artificial empirical model, MOGWO significantly reduces the reservoir pressure by 0.73%; (2) in terms of solution quality, the spacing (Sp) and inverse generation distance (IGD) of MOGWO are optimized by 64.90% and 80.87% respectively compared with NSGA-II, proving that it has stronger global search ability; (3) by analyzing the Pareto front and reservoir fluid distribution, the strong nonlinear characteristics of the pressure-flow relationship are revealed, and traditional empirical formulas are difficult to accurately characterize such complex relationships. This study confirms that the bionic intelligent algorithm can effectively solve the UGS multi-objective optimization problem driven by multi-physical field coupling, providing a data-driven decision-making tool for gas injection scheduling.