Intergranular Pore Structures in Sandstones

• https://doi.org/10.1002/grl.50771

Abstract — While geological carbon dioxide (CO2) storage could contribute to reducing global emissions, it must be designed such that the CO2 cannot escape from the porous rock into which it is injected. An important mechanism to immobilize the CO2, preventing escape, is capillary trapping, where CO2 is stranded as disconnected pore‐scale droplets (ganglia) in the rock, surrounded by water. We used X‐Ray microtomography to image, at a resolution of 6.4 µm, the pore‐scale arrangement and distribution of trapped CO2 clusters in a limestone. We applied high pressures and temperatures typical of a storage formation, while maintaining chemical equilibrium between the CO2, brine, and rock. Substantial amounts of CO2 were trapped, with an average saturation of 0.18. The cluster sizes obeyed a power law distribution, with an exponent of approximately −2.1, consistent with predictions from percolation theory. This work confirms that residual trapping could aid storage security in carbonate aquifers.

• https://doi.org/10.1007/s10596-018-9768-y

Abstract — Three sets of synthetic images were created from two original datasets. A suite exhibiting greyscale contrast was produced from an 8.96-μm voxel size 3D X-ray microscopy image of a sandstone rock and a two suites (one showing greyscale contrast and one showing both greyscale and textural contrast) were produced from a 5 × 5 × 5 nm voxel size FIB-SEM image of a shale rock. The performance of three image segmentation algorithms (global multi-Otsu thresholding, seeded watershed region growing, and machine learning-based multivariant classification) was then assessed by their ability to recover their respective original segmented 3D images. While all algorithms performed well at low noise levels, machine learning-based classification proved significantly more noise tolerant than either of the traditional algorithms. It was also able to segment the non-greyscale (textural based) contrast, something the traditional completely failed to do, with voxel misclassification rates for the traditional techniques above 50% at a 0 noise level within the textural contrast regions. Machine learning-based classification, in contrast, achieved misclassification rates of less than 5% in the same regions.

• https://doi.org/10.1029/2019WR025171
• 10.1029/2019WR025171

Abstract — We statistically infer fluid flow and transport properties of porous materials based on their geometry and connectivity, without the need for detailed material properties and expensive physical simulations. Our predictions are consistent with traditional approaches. We summarize structure by persistent homology, then determines the similarity of structures using image analysis and statistics. Longer term, this may enable quick and automated categorization of rocks into known archetypes. We first compute persistent homology of binarized 3D images of material subvolume samples. The persistence parameter is the signed Euclidean distance from inferred material interfaces, which captures the distribution of sizes of pores and grains. Each persistence diagram is converted into an image vector. We infer structural similarity by calculating image similarity. For each image vector, we compute principal components to extract features. We fit statistical models to features estimates material permeability, tortuosity, and anisotropy. We develop a Structural SIMilarity index (SSIM) to determine Statistical Representative Elementary Volumes (sREV).