Abstract
Numerical simulation is the most commonly used method to predict the power generation capacity of EGS during geothermal energy extraction. However, it is time-consuming to optimize the scheme only by comparing the numerical simulation methods, and it is difficult to determine the globally optimal operation strategy. In this study, five key parameters including well spacing, water injection rate, injection temperature, fracture permeability and fracture spacing are considered. Based on the numerical simulation data, optimized Back-Propagation Neural Network (BPNN) prediction models combining the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) were developed to investigate the impact of various factors on the heat recovery performance of a three-horizontal-well EGS in the Zhacang geothermal field. On the basis of these PSO-GA-BPNN models, the weights of the evaluation indexes for each geothermal development were calculated by hierarchical analysis method. In this study, an innovative combination of numerical simulation, PSO-GA-BPNN model, and Analytic Hierarchy Process was proposed to establish an EGS comprehensive optimization method, effectively improving the accuracy and computational efficiency of scheme optimization. The results reveal that predicting EGS with PSO-GA-BPNN models has a good prediction accuracy for each performance index. After a comprehensive comparison, the combination of well spacing of 600 m, water injection rate of 27 kg/s, injection temperature of 58 ℃, fracture permeability of 1 × 10(-10) m(2) and fracture spacing of 100 m was identified as the optimal power generation scheme. The EGS power plant is expected to have an installed capacity of 6.05-8.17 MW, with a total generating capacity of 3,163.16 GWh and a levelized cost of electricity of $0.033/kWh. The method is very effective in the development and optimal design of geothermal systems and can also provide a reference for other geothermal projects.