| Abstract |
An efficient numerical model, which can simulate the fully coupled thermo-hydro-mechanical-chemical (THMC) processes in geothermal reservoirs, is essentially required to evaluate the fate of geothermal wells and the performances of geothermal reservoirs in response to the long-term well operations. In this study the latest developments in robust numerical modeling for geothermal reservoirs are presented. First, an integrated model combining the simplified 1D geothermal well model and the 3D geothermal reservoir model is developed to simulate the coupled mechanical deformation, fluid flow and heat transfer processes in geothermal reservoirs including multiple wells. The simplified 1D geothermal well model considers the heat convection and conduction along well axis and heat and mass exchange between geothermal fluid and reservoir rocks in radial direction. Second, A modeling framework for the coupled THMC processes in fractured geothermal reservoirs is developed to simulate the stimulation of acid fracturing. Finally, a surrogate model based on machine learning technique is developed to reduce the substantial computational burden of forward simulations, which is then combined with genetic algorithm to develop a robust optimization approach for well placement of geothermal doublets in heterogeneous geothermal reservoirs. A number of cases studies including the Rongcheng geothermal field, Dezhou geothermal field, and Tongzhou district in China are presented to demonstrate the robustness and accuracy of the developed numerical methods. |