学术文献库

Pore-network modeling is a widely used predictive tool for pore-scale studies in various applications that deal with multiphase flow in porous media. Despite recent improvements to enable pore-network modeling on simplified pore geometry extracted from rock images and its computational efficiency compared to direct numerical simulation methods, there are still limitations to modeling a large representative pore-network for heterogeneous cores. These are due to the technical limits on sample size to discern void space during imaging and computational limits on pore-network extraction algorithms. Thus, there is a need for pore-scale modeling approaches that have the natural advantages of pore-network modeling and can overcome these limitations, thereby enabling better representation of heterogeneity of the core and enhancing the accuracy of prediction of properties. This paper addresses these issues with a workflow that includes a novel pore-network stitching method to provide large-enough representative pore-network. It uses CT images of heterogeneous cores at different resolutions to characterize the pore structure in order to select few signature parts and extract their pore-networks. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a layered stitching method. We validate all steps of this method on different types of rocks based on flow properties such as relative permeability. Then, we apply the stochastic workflow on two large heterogeneous domain problems. We generate multiple realizations and compare the average results with a reference pore-network for each problem. We demonstrate that signature parts of a heterogeneous core, which are a small portion of its entire volume, are sufficient inputs for the developed method to construct a representative pore-network.