ABSTRACT: The multi-stage enhanced geothermal system (EGS) is expected to become an important technology for the development of hot dry rock (HDR) geothermal resources.Complex fracture networks are a critical factor influencing EGS performance, yet research on optimizing these networks remains limited, constraining extraction efficiency. This study develops a multi-stage EGS numerical model based on a thermo-hydro-mechanical(THM)coupling approach. By comparing EGS performance under fracture networks with different primary artificial fractures, secondary fractures and natural fractures,The study investigates the influences of key fracture parameters, including the number of primary fractures, angle of secondary fracture, and the permeability of natural fractures. A multi-objective optimization framework is established by integrating the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM). Utilizing multi-objective optimization, production temperature, flow rate, and accumulative energy are optimized by systematically balancing the key fracture parameters .The results demonstrate that Artificial fractures play a crucial role in EGS production, and selecting appropriate fracture parameters can effectively enhance heat extraction efficiency.The optimized extraction strategy achieves significant improvements compared to the base case, with extraction efficiency increased by over 30%. Additionally, production temperature rises by 5.1%, flow rate improves by 34.2%, and accumulative energy increases by 40.6%. These findings highlight the potential of the optimized fracture design to enhance the efficiency of geothermal resource extraction, providing valuable insights for the development of EGS systems.
Zhou et al. (Sun,) studied this question.
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