Multiscale fusion of tight sandstone digital rocks using attention-guided generative adversarial network

Abstract

Tight sandstone exhibits heterogeneity due to its multiple mineral compositions and multiscale pore structures, presenting significant challenges for digital rock modeling. To capture the multiscale information inherent in heterogeneous rocks, we utilized an attention-guided generative adversarial network (AttentionGAN) to combine X-ray micro-computed tomography (micro-CT) and scanning electron microscope (SEM) images of tight sandstone, thereby producing large-scale, high-precision rock images. The reconstructed digital rock images comprehensively showcase the multiscale pore structures, including intergranular and clay micropores, as well as the distribution of minerals. Mineral content, two-point correlation functions, representative volume elements, and pore radius distributions were calculated for both the real and reconstructed images to confirm the efficacy of the proposed AttentionGAN. The results demonstrate that AttentionGAN is capable of generating high-precision rock images with a wide field of view. This offers a beneficial methodology for characterizing heterogeneous rocks and numerically simulating rock physics processes.