Prediction of two-phase flow properties for digital sandstones using 3D convolutional neural networks

Abstract

Multiphase fluid flow within porous media is of great importance in a wide range of environmental and industrial fields, such as CO2 sequestration, geothermal systems, fuel cells, enhanced oil recovery, and groundwater remediation. Pore-scale modeling is a promising tool to estimate macroscopic multiphase flow properties from micro-scale images of porous materials; however, it is a complex, time-consuming procedure and would be highly resource-intensive in the case of direct numerical simulation. We present a framework that consists of (1) extraction of sub-samples from rock images, (2) computation of relative permeability and capillary pressure from two-phase pore network modeling with respect to their contact angle and interfacial tensions, (3) training a convolutional neural network (CNN), and (4) validation with unseen datasets and parameters. 500 sub-samples of grayscale and binary types are extracted from images of 12 different sandstones. Relative permeability, capillary pressure, and residual saturation of binary sub-samples are computed from two-phase fluid flow simulation in their representative pore network models The proposed CNN model is trained with grayscale images, eliminating the need for image processing. The results demonstrate a good agreement between CNN predictions and simulation results, and the computational time was reduced by several orders of magnitude.