Data-Driven Models to Predict Hydrocarbon Production From Unconventional Reservoirs by Thermal Recovery

In the numerical simulations of thermal recovery for unconventional resources, reservoir models involve complex multicomponent-multiphase flow in non-isothermal conditions, where spatial heterogeneity necessitates the huge number of discretized elements. Proxy modeling approaches have been applied to efficiently approximate solutions of reservoir simulations in such complex problems. In this study, we apply machine learning technologies to the...