Methods for accelerated development planning optimization using machine learning for unconventional oil and gas resources

Abstract

Methods for analyzing subsurface process data in order to perform one or more subsurface operations in a subsurface are provided. Generating subsurface models is typically a long and laborious process in which subsurface process data is analyzed in order to generate the subsurface models. In contrast, work in generating the subsurface models may be front-loaded by first using a physics simulator in order to generate a training set of subsurface forward models, and then performing machine learning using the training set to generate one or more proxy models, such as a forward proxy model and an inverse proxy model. The machine learning may be constrained using physics-based rules to better converge on the proxy models. In this way, the already-trained inverse proxy model may input the subsurface process data in order to generate potential inverse models, which may then be used to perform subsurface operations in the subsurface.

Dependencies are inferred from the claim text.