Downscaling‐Based Segmentation for Unresolved Images of Highly Heterogeneous Granular Porous Samples

Abstract Numerical simulations of pore‐scale flow and transport in natural sediments require the knowledge of pore‐space topology. Limited resolution of X‐ray tomography is often insufficient to fully characterize pore‐space structure within fine‐grained regions. Single and multilevel threshold‐based segmentation approaches are customarily employed to identify solid, pore and porous‐solid regions by means of grey intensity thresholds. While the choice of cutoff thresholds is often arbitrary, it dramatically affects the effective properties and the dynamical response of the reconstructed porous structure. We propose an algorithm of downscaling, i.e., the process of increasing image resolution, followed by segmentation, i.e., the identification of different phases, to reconstruct the unresolved pore‐space from XCT images of natural geological porous media. The method, applicable to moderately unresolved, chemically homogeneous granular media, is based on a map between local pixel porosity and pore size that does not rely on the definition of arbitrary thresholds and it allows to generate a high‐resolution binary image of the porous medium from poorly resolved grey‐scale images. First, we validate the method on synthetic unresolved images and compare their known pore‐space distribution with the extracted one. Then, we consider a synthetic porous medium and compare the pore size distribution, conductivity, and tortuosity between the original and the reconstructed structures. Finally, we apply the method to extract the pore‐space distribution from unresolved XCT images of two natural sediment columns and use it (i) to parametrize a capillary‐bundle model and (ii) to estimate the hydraulic conductivity by matching breakthrough behavior of passive solute transport.