Fracture Network Characterization and Thermal Performance Prediction in Enhanced Geothermal Reservoirs Using Covariance Matrix Adaptation and Embedded Discrete Fracture Model

Abstract Fracture networks constitute essential conduits for fluid and heat transport in enhanced geothermal systems (EGSs), yet their characterization is challenging due to the inherent geological complexities. This study develops an integrated inversion framework for effective fracture network characterization. The framework consists of a novel fracture network parameterization method, the covariance matrix adaptation‐evolution strategy (CMA‐ES) for fracture parameter inversion, and the embedded discrete fracture model (EDFM) for robust forward simulation of flow and transport in fractured reservoirs. The proposed fracture network parameterization method uses a background fracture network with fixed geometries and non‐uniform fracture apertures to approximate real‐world fracture networks. CMA‐ES is employed to infer fracture parameters by matching both conservative and sorptive tracer measurements, and multiple parallel CMA‐ES runs are executed to obtain an ensemble of model realizations for uncertainty assessment. Three synthetic EGS case studies with varying complexities demonstrate the effectiveness of the inversion framework in capturing major flow and transport characteristics in fracture networks. The long‐term thermal performances of the EGS reservoirs are appropriately predicted with the inferred fracture network models. This integrated framework offers a feasible solution for fracture network characterization and thermal performance prediction in EGS and also has potential applications in unconventional gas/oil reservoir explorations.