Mitigating greenhouse gas emissions by underground carbon dioxide storage or by coupling intermittent renewable energy with underground hydrogen storage are solutions essential to the future of energy. Of particular importance to the success of underground storage is the fundamental understanding of geochemical reactions with mineralogical phases and flow behavior at the length scale at which interfaces are well resolved. Fast synchrotron-based three-dimensional x-ray microcomputed tomography (µCT) of rocks is a widely used technique that provides real-time visualization of fluid flow and transport mechanisms. However, fast imaging results in significant noise and artifacts that complicate the extraction of quantitative data beyond the basic identification of solid and void regions. To address this issue, an image-processing workflow is introduced that begins with unpaired domain transfer by cycle-consistent adversarial network, which is used to transfer synchrotron-based µCT images containing fast-imaging-associated noise to long-scan high-quality µCT images that have paired ground truth labels for all phases. The second part of the workflow is multimineral segmentation of images using convolutional neural networks (CNNs). Four CNNs are trained using the transferred dynamic-style µCT images. A quantitative assessment of physically meaningful parameters and material properties is carried out. In terms of physical accuracy, the results show a high variance for each network output, which indicates that the segmentation performance cannot be fully revealed by pixel-wise accuracy alone. Overall, the integration of unpaired domain transfer with CNN-based multimineral segmentation provides a generalizable digital material framework to study the physics of porous materials for energy-related applications, such as underground ${\mathrm{CO}}_2$ and ${\mathrm{H}}_2$ storage.

• Received 23 November 2021
• Revised 22 February 2022
• Accepted 1 March 2022

DOI:https://doi.org/10.1103/PhysRevApplied.17.034048