High-quality digital rock images are essential for subsequent high-precision numerical simulations. But limited by the imaging capability of computed tomography (CT), high resolution digital rock images with wide imaging field of view (FOV) cannot be acquired simultaneously. To cope with this constraint, we propose a novel Multi Attention Super-Resolution Neural Network (MASR) that enhances the resolution of images with wide FOV. Considering that textures and edges are more crucial in digital rocks, MASR introduces the component attention mechanism of Component Divide-and-Conquer Super-Resolution (CDCSR) model. By redesigning the hourglass network with spatial and channel attention mechanisms, proposing a spatial attention-based mask module, and optimizing the component attention mask calculation process, MASR delivers higher information utilization with fewer parameters and faster training than CDCSR. And we optimize the depth of MASR to trade off speed and super-resolution quality. Furthermore, we retrained several state-of-the-art models. Through quantitative evaluations and qualitative visualizations, it is verified that MASR can recover sharper edges while removing noise, and obtain digital rock images with superior quality and reliability. The pixelwise relative errors of MASR reconstructions are reduced by 15% to 26% over bicubic interpolation method. Our codes are publicly available at https://github.com/MHDXing/MASR-for-Digital-Rock-Images.

This study presents a novel model to predict gas-water two-phase transport behaviors in shale microfractures by incorporating a mobile water film with varying thickness according to the extended Derjaguin-Landau-Verwey-Overbeek (DLVO) theory as well as multiple fluid transport mechanisms (i.e., real gas transport controlled by the Knudsen number and water slippage). This model is implemented in real shale microfractures via digital-core imaging. A gas-water displacement process is modelled by the invasion percolation theory, while a local multiphase distribution is determined by combining disjoining pressure with capillary force. Key findings reveal that gas relative permeability (RP) decreases by 17% and water RP enhances by 33.5%, when the mean aperture decreases from 1.67 to 0.0418μm. Neglecting water film brings a decrease in water RP and an overestimation of gas transport ability. Moreover, two critical microfracture apertures are determined, which enhances an understanding of the water film impact on gas-water transport properties in application.