Effective permeability of an immiscible fluid in porous media determined from its geometric state

作     者：Alzubaidi, Fatimah Mostaghimi, Peyman Niu, Yufu Armstrong, Ryan T. Mohammadi, Gelareh McClure, James E. Berg, Steffen 

作者机构：School of Minerals and Energy Resources Engineering University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia National Security Institute Virginia Tech BlacksburgVA24061 United States Shell Global Solutions International B.V. Grasweg 31 Amsterdam1031HW Netherlands 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Pore structure 

摘      要：Based on the phenomenological extension of Darcy s law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure and wettability. For applications, fuel cells to underground CO2 storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with R2 = *** Codes 76S05 (Primary), 76T06 (Secondary) Copyright © 2022, The Authors. All rights reserved.