Generalized regression neural network-assisted synergistic optimization of oil recovery, heat extraction, and carbon sequestration in deep reservoirs

Deep reservoirs have shown an advantage in synergistic oil production, geothermal development, and carbon sequestration, with great economic potential. To solve the problems of high-dimensional variables and poor convergence in the collaborative optimization of these three processes, this paper proposes a new algorithm called GRNN-assisted Surrogate Optimization Algorithm (GSOA) based on the generalized regression neural network (GRNN) classifier and the surrogate model co-optimization. GSOA enhances search space diversity via GRNN hierarchical prescreening and combines a Radial Basis Function surrogate model with Differential Evolution for efficient global optimization, mitigating computational inefficiency and local optima in high-dimensional problems. A numerical model for medium-high temperature reservoirs (56 × 45 × 7 grid, 115 °C, 45 MPa) was established, optimizing bottom hole pressures for net present value (NPV) maximization while balancing oil recovery, CO2 storage, and geothermal energy production. Simulations demonstrate that GSOA outperforms traditional surrogate-assisted optimization (SOA), achieving a 3.9% increase in cumulative oil production (284.9 × 103 m3 vs 274.3 × 103 m3), 5.2% higher CO2 storage (65.3 × 106 m3 vs 62.1 × 106 m3), 0.9% enhanced heat extraction (2.72 × 1013 J vs 2.69 × 1013 J), and 3.2% greater NPV (1.21 × 108 CNY vs 1.17 × 108 CNY). GSOA converges to peak NPV in 55 generations—52% faster than SOA (115 generations)—validating its robustness in CCUS (Carbon Capture, Utilization and Storage)-integrated geothermal systems. This research advances methodologies for CO2-enhanced oil recovery-geothermal synergy and supports carbon-neutral resource utilization.