An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

Abstract

AbstractHigh‐resolution X‐ray microcomputed tomography (micro‐CT) data are used for the accurate determination of rock petrophysical properties. High‐resolution data, however, result in a small field of view, and thus, the representativeness of a simulation domain can be brought into question when dealing with geophysical applications. This paper applies a cycle‐in‐cycle generative adversarial network (CinCGAN) to improve the resolution of 3‐D micro‐CT data and create a super‐resolution image using unpaired training images. Effective porosity, Euler characteristic, pore size distribution, and absolute permeability are measured on super‐resolution and high‐resolution ground‐truth images to evaluate the physical accuracy of the proposed CinCGAN. The results demonstrate that CinCGAN provides physically accurate images with an order of magnitude larger field of view when compared to typical micro‐CT methods. This unlocks new pathways for the geophysical characterization of subsurface rocks with broad implications for flow modeling in highly heterogeneous rocks or fundamental studies on nonlocal forces that extend beyond domain sizes typically used for pore‐scale simulation.