SmartRocks: Artificial Intelligence Applications in Digital Rock Physics

Abstract

The characterization of subsurface rock’s physical properties plays a crucial role in multiple fields, including geophysics, petroleum, carbon capture & storage, and water resource management. In recent years, Digital Rock Analysis (DRA) has emerged as an effective method for rock characterization, offering cost-effective, non-destructive, and digital assessments of rock samples. This thesis delivers an in-depth study of artificial intelligence (AI) applications for the field of DRA. The work presented here aims to advance image analysis and generation techniques with the potential to revolutionize current methods and significantly enhance the analysis capabilities of DRA. The thesis covers multiple projects and four publications that address key problems within the field of DRA. These problems revolve around achieving accurate segmentation, enhancing image resolution, conducting precise image registration, and generating 3D microstructures from 2D images. To tackle these problems, advanced deep learning techniques such as generative adversarial networks (GANs), transformers, and denoising diffusion probabilistic models (DPM) have been employed. With a strong focus on industry applications, the methodologies presented in this thesis emphasize generalization, scalability, and robustness. This emphasis is particularly crucial when dealing with large-scale 3D image data in diverse digital rock analysis (DRA) scenarios. The research outcomes have been successfully integrated into the SmartRock platform, a web-based service platform, thereby enhancing accessibility for researchers and engineers to utilize the models and tools proposed in this thesis. By integrating cutting-edge AI technologies into the DRA industry, this research aims to improve DRA workflows, streamline processes, and promote the overall progress of the field, leading to greater efficiency, cost reduction, and enhanced characterization accuracy.