Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks

Micro computed tomography (CT) provides petrophysics laboratories with the ability to image three dimensional porous media at pore scale. However, evaluating flow properties requires the acquisition of a large number of representative images, which is often unfeasible. Stochastic reconstruction methods are algorithms able to generate novel, realistic rock images from a small sample, thus avoiding a large acquisition process. A more convenient approach would use only two dimensional images, replacing 3D scans with images of the rock cuttings made during the drilling. This would extend the technique to media having pores smaller than the resolution of the micro-CT, but that can be imaged by microscopy. We introduce a novel method for 2D-to-3D reconstruction of the structure of porous media by applying generative adversarial neural networks. We compare several measures of pore morphology between simulated and acquired images. Experiments include bead pack, Berea sandstone, and Ketton limestone images. Results show that our GANs-based method can reconstruct three-dimensional images of porous media at different scales that are representative of the morphology of the original images. Also, compared to classical stochastic methods of image reconstruction, the generation of multiple images is much faster. (c) 2020ElsevierB.V. Allrightsreserved.