Enhancing digital rock analysis through generative artificial intelligence: Diffusion models

Abstract

Within the realm of computer vision, the landscape has been significantly reshaped by the abundance of extensive and diverse datasets, leading to remarkable breakthroughs in image processing. These advancements have reverberated across a wide spectrum of applications, catalyzing transformative outcomes. However, in stark contrast, the field of digital rock analysis finds itself grappling with a conspicuous dearth of data, a challenge that casts a formidable shadow over the effective deployment of computer vision techniques for rock image analysis. In response to this pressing issue, this paper presents a pioneering methodology designed to surmount the hurdles posed by data limitation in the realm of digital rock analysis. At the core of this innovative approach lies the fusion of artificially generated digital rock images, created using a state-of-the-art diffusion model, with their authentic counterparts. This fusion is guided by the overarching objective of augmenting the efficacy of various digital rock analysis applications. This integration endeavors to bridge the gap between the limited available data and the substantial demands of the digital rock analysis domain. The practical significance and potential of this integrated approach are vividly demonstrated through a series of concrete implementations. These include, but are by no means limited to, enhancing image quality to facilitate clearer visualization of intricate rock structures and refining the estimation of petrophysical properties with increased accuracy.