3D-porous-GAN: a high-performance 3D GAN for digital core reconstruction from a single 3D image

Abstract

The 3D digital rock technology is extensively utilized in analyzing rock physical properties, reservoir modeling, and other related fields. This technology enables the visualization, quantification, and analysis of microstructures in rock cores, leading to precise predictions and optimized designs of reservoir properties. Although the accuracy of 3D digital rock reconstruction algorithms based on physical experiments is high, the associated acquisition costs and reconstruction processes are expensive and complex, respectively. On the other hand, the 3D digital rock random reconstruction method based on 2D slices is advantageous in terms of its low cost and easy implementation, but its reconstruction effect still requires significant improvement. This article draws inspiration from the Concurrent single-image generative adversarial network and proposes an innovative algorithm to reconstruct 3D digital rock by improving the generator, discriminator, and noise vector in the network structure. Compared to traditional numerical reconstruction methods and generative adversarial network algorithms, the method proposed in this paper is shown to achieve good agreement with real samples in terms of Dykstra-Parson coefficient, porosity, two-point correlation function, Minkowski functionals, and visual display.