A new approach for predicting the thermal performance of enhanced geothermal systems

Enhanced Geothermal Systems (EGS) offer a viable pathway for sustainable energy production, especially in hot dry rock (HDR) reservoirs. This study proposes a novel predictive model to estimate the long-term thermal performance of EGS across various working fluids, including water, supercritical carbon dioxide (SC-CO 2 ), and nitrous oxide (N 2 O). The model integrates key thermophysical properties and reservoir parameters while accounting for time-dependent temperature degradation and energy extraction dynamics. Validation is performed using two established case studies, wherein the model demonstrates high fidelity in predicting performance over a 30-year operational period. Parametric analysis revealed the influence of injection temperature and mass flow rate on system performance, highlighting N 2 O and SC-CO 2 as promising alternatives to water under optimized conditions. Importantly, the proposed model is not intended to replace high-fidelity simulators but rather to complement them as a lightweight, rapid-assessment tool for early-stage design and fluid substitution decisions. The framework provides a practical and scalable approach to support geothermal energy optimization in sustainable energy planning.