Using Physics Informed Generative Adversarial Networks to Model 3D porous media

Abstract

Micro-CT scanning of rocks significantly enhances our understanding of pore-scale physics in porous media. With advancements in pore-scale simulation methods, such as pore network models, it is now possible to accurately simulate multiphase flow properties, including relative permeability, from CT-scanned rock samples. However, the limited number of CT-scanned samples and the challenge of connecting pore-scale networks to field-scale rock properties often make it difficult to use pore-scale simulated properties in realistic field-scale reservoir simulations. Deep learning approaches to create synthetic 3D rock structures allow us to simulate variations in CT rock structures, which can then be used to compute representative rock properties and flow functions. However, most current deep learning methods for 3D rock structure synthesis don't consider rock properties derived from well observations, lacking a direct link between pore-scale structures and field-scale data. We present a method to construct 3D rock structures constrained to observed rock properties using generative adversarial networks (GANs) with conditioning accomplished through a gradual Gaussian deformation process. We begin by pre-training a Wasserstein GAN to reconstruct 3D rock structures. Subsequently, we use a pore network model simulator to compute rock properties. The latent vectors for image generation in GAN are progressively altered using the Gaussian deformation approach to produce 3D rock structures constrained by well-derived conditioning data. This GAN and Gaussian deformation approach enables high-resolution synthetic image generation and reproduces user-defined rock properties such as porosity, permeability, and pore size distribution. Our research provides a novel way to link GAN-generated models to field-derived quantities.