Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous media

Abstract In this study, coupled Generative Adversarial and Auto-Encoder neural networks have been used to reconstruct realizations of three-dimensional porous media. The gradient-descent-based optimization method is used for training and stabilizing the neural networks. The multi-scale reconstruction has been conducted for both sandstone and carbonate samples from an Iranian oilfield. The sandstone contains inter and intra-grain porosity. The generative adversarial network predicts the inter-grain pores and the auto-encoder provides the generative adversarial network result with intra-grain pores (micro-porosity). Different matching criteria, including porosity, permeability, auto-correlation function, and visual interpretation have been used to investigate the performance of the models. This methodology provides researchers with a reliable method to reconstruct multi-scale realizations of porous media.