Fast prediction of methane adsorption in shale nanopores using kinetic theory and machine learning algorithm

Abstract

Understanding the gas adsorption behavior in shale nanopores is essential for the reservoir estimation and performance prediction of shale gas, which is still unclear considering the complexity of geological environment and nanoporous structure of shale. In general, traditional methods based on experiments and molecular dynamics (MD) simulations are always expensive and time consuming. In this work, a machine learning (ML) framework to predict the methane adsorption behavior in shale nanopores is constructed from the microscopic and kinetic theory perspectives, where three novel parameters related to potential energy distribution (PED) are proposed to represent the methane adsorption characteristics of shale slit nanopores. Machine learning algorithm based on the uniformly constructed dataset is introduced to realize fast and accurate prediction of methane adsorption behavior, which is well validated by typical inorganic and organic models. Moreover, the application of the proposed ML model to predict the adsorption behavior of methane in different geological conditions (e.g., pressure and temperature) is performed, indicating its feasibility to predict the gas adsorption behavior in shale nanopores with ultra-fast computation speed, that would be beneficial for the exploitation and development of shale gas reservoirs. The insights gained in this work is also instructive for the prediction of nano-confined fluid behavior under complex environmental factors.