  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.

Computational Physics;Fluid Dynamics;Geophysics