Pore structure properties characterization of shale using generative adversarial network: Image augmentation, super-resolution reconstruction, and multi-mineral auto-segmentation

Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view, and manual multi-mineral segmentation in shale lacks precision. To address these limitations, we propose a comprehensive framework based on generative adversarial network (GAN) for characterizing pore structure properties of shale, which incorporates image augmentation, super-resolution reconstruction, and multi-mineral auto-segmentation. Using real 2D and 3D shale images, the framework was assessed through correlation function, entropy, porosity, pore size distribution, and permeability. The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8, without paired shale images, effectively reconstructing the unresolved fine-scale pores under a low resolution, rather than merely denoising, deblurring, and edge clarification. The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results, resulting in a strong resemblance to real samples in terms of pore size distribution and permeability. This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media, such as carbonate, coal, and tight sandstone reservoirs.