New 2D to 3D Reconstruction of Heterogeneous Porous Media via Deep Generative Adversarial Networks (GANs)

Abstract

Accurately characterizing rock microstructures in three dimensions (3D) is crucial for modeling various physical phenomena and estimating rock properties. Despite advancements in 3D imaging, limitations arise from the trade‐off between sample size and resolution, particularly in heterogeneous rocks with multi‐scale features where both high resolution and a large field of view are essential. These challenges have prompted interest in accurate 3D reconstructions from high‐resolution two‐dimensional (2D) images using advanced generative models like generative adversarial networks (GANs). In this study, using scanning electron microscopy and optical microscopy, we acquired 2D images from three orthogonal sections of a Berea sandstone sample. These images were employed to train a modified SliceGAN model, a variant of GANs, for 3D reconstruction. Unlike previous studies utilizing methods for 2D to 3D reconstructions that typically incorporated 3D images in their training, our approach relies exclusively on 2D images. We propose a systematic workflow which enables us to produce 3D reconstructions that closely mirror the original 2D inputs and a 3D X‐ray tomography in terms of structural and morphological characteristics. Additionally, we highlight the importance of input data size and training our model with representative images which enable us to generate diverse reconstructions with transport properties that align with previous studies on Berea sandstone. This underscores the potential of 2D to 3D reconstructions as an effective alternative to multiple X‐ray tomographies, integral for assessing variability in heterogeneous rocks.