Probing fractured reservoir of enhanced geothermal systems with fuzzy-genetic inversion model: Impacts of geothermal reservoir environment

Our previous study (Zhou et al., 2023) proposed a fuzzy inference model to inverse the predominant flow area in fractured reservoirs. Yet, geothermal reservoir parameters in the direction problem model are hard to make exactly consistent with the actual parameters. This study aims to discuss the impacts of discrepancies between actual geothermal reservoir environment and simulated reservoir environment on the inversion results. An inversion model (including direction problem model, fuzzy inference model, and genetic algorithm) is established to explore the location of predominant flow area under different guess geothermal reservoir environments. The effects of running time, guess reservoir environment (temperature field, pressure field, and Hot Dry Rock property) and guess predominant fracture area property on inversion results are studied. Results show that longer running time can obtain good inversion results, and the inversion speed decreases as running time increases. In addition, the inversion model has good inversion accuracy under different guess geothermal reservoir environments. Lastly, the inversion accuracy is very poor when the guess permeability of predominant fracture area is extremely low. However, the inversion model still needs improvement in the initial stage. It cannot inverse the location of predominant flow area when the running time is less than 1×107 s. •Fuzzy-genetic inversion model is established to explore fractured reservoir.•Effects of geothermal reservoir environment on inversion results are studied.•Running time can determine inversion speed and inversion result accuracy.•Geothermal reservoir environment has slight effects on inversion results.