Enhancing identification of digital rock images using super-resolution deep neural network

Abstract

Identification of pore and minerals from digital rock images plays an important role in characterization of oil and gas reservoirs. Traditional methods usually require high costs of time and labor. With the rapid development of artificial intelligence, deep learning methods have been applied to geosciences. Among them, semantic segmentation neural networks are used to identify rock images, but traditional semantic segmentation networks require high-resolution input images to improve performance and can only generate masks with the same resolutions as the input images, which again requires high-performance equipment and increases computational and equipment costs. In this paper, we propose an approach to enhancing identification of digital rock images with super-resolution neural network (SSRN) which enables low-resolution input images to generate high-resolution representations. We use SSRN to train the 10-classification thin-section images, and compare it with traditional deep learning methods. It indicates that SSRN can effectively improve the performance of the generated high-resolution masks.