Geothermal Reservoir Optimization Empowered by a Generalized Thermal Decline Model and Deep Learning

Abstract

Abstract Geothermal energy is naturally renewable harnessed from subsurface reservoirs and is feasible to help enrich the energy spectrum and decarbonize the economy. Cold geo-fluid such as water is injected to extract the heat from hot rocks, and then hot fluid can be produced, which can be used for the purpose of heating or power generation. The reservoir management of geothermal recovery process is an integration of geology, drilling, reservoir and production, and particularly it requires expensive simulations that couple the thermo-hydro-mechanical (THM) effect. In this study, we developed a reservoir simulation model to simulate the enhanced geothermal systems (EGS). After evaluating the produced fluid temperature curves, we proposed a generalized thermal decline model that considers the thermal breakthrough and the following decline behavior. This model is parsimonious with only 3 variables. Moreover, a forward surrogate model by deep neural network is developed to predict the decline model variables and the ultimate total net power based on the reservoir parameters. The forward surrogate is integrated with a differential evolution optimizer, which considers reservoir uncertainties and nonlinear constraints for the optimization of the total net power. Accelerated by the thermal decline model and forward surrogate model, we were able to efficiently perform reservoir optimization in high-performance computing environment, and this makes the workflow quite scalable for real-time reservoir management.