On Representative Elementary Volumes of Grayscale Micro‐CT Images of Porous Media

Abstract

The concept of linking pore‐scale data to continuum‐scale characteristics of porous media relies on the existence of a representative elementary volume (REV). The current techniques for estimating REVs require access to segmented micro‐computed tomographic (micro‐CT) images and computations of petrophysical properties which are computationally intensive and time‐consuming. Herein, a texture characterization method called the Gray‐Level Size Zone Matrix (GLSZM) is applied directly to raw grayscale micro‐CT images. GLSZM representations of 3D micro‐CT images capture information regarding the connectivity of gray‐level intensities, termed as “size‐zones.” Statistical descriptors of pore space are calculated based on GLSZM to understand the connectivity of low gray‐level intensities. These GLSZM statistics capture microstructural fluctuations and offer insights into the impact of grayscale heterogeneity on REV size. This approach allows REV sizes to be estimated directly using grayscale micro‐CT images, in a reproducible, less time‐consuming and computationally efficient manner.

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

“…A distinctive advantage of GLSZMs is that, unlike the histogram‐based representations, it also highlights both dominant and minor features. Dominant features occur as peaks with high‐to low‐connectivity whereas minor features occur as medium‐to low‐connectivity peaks (Singh, 2020).…”

“…Then, second-order statistics from GLCM were calculated to better understand rock structure in terms of grain sizes, anisotropy and heterogeneity. Similarly, another study by Singh (2020) explores the use of GLSZMs for calculating statistical measures that can be used as analogs to porosity and permeability. Using these statistical analogs, one can infer grayscale representative elementary volumes (GREVs) in an efficient and robust manner.…”

“…Generally speaking, pores or empty spaces are represented by low gray‐level intensity values, followed by high intensity components, such as silicate, calcite, or bauxite and lastly, the very‐high intensity ingredients, such as iron oxides, pyrite, or barite. This variation in the gray‐level intensities of the porous material can be analyzed using three main methods: (a) Histograms, (b) Gray‐level co‐occurrence matrix (GLCM; Hall‐Beyer, 2017a, 2017b; Haralick, 1979; Haralick et al., 1973; Haralick & Shanmugam, 1973; Singh et al., 2019), and (c) Gray‐level size‐zone matrix (GLSZM; Singh, 2020; Thibault, 2009; Thibault et al., 2014).…”

Microscopic Characterizations of Heterogeneous Pores, ITZs, Multiple‐Solids, and Their Impacts on Damage Property of Sandstone by Low‐High Resolution 3D Reconstruction

“…In the X-ray μ-CT imaging experiments, the manufactured standard samples are respectively scanned by the Multi-function-CD90BX X-ray μ-CT imaging apparatus. The scanning electricity and voltage for the low resolution imaging are respectively determined as 130 kV and 81 μA and they are respectively considered as 10.1029/2021GL095001 3 of 11 190 kV and 118 μA for the high-resolution imaging due to the trade-offs between the field of view and resolutions (Singh et al, 2019) for the scanned sandstone samples. Moreover, the spatial resolutions of sample S1 and sample S2 for the low resolution and high-resolution imaging are respectively 5.22 and 1.74 μm, as shown in Figure S1b.…”

An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

“…Unfortunately, imaging technologies used to generate numerical domains for simulation have an inherent trade‐off between resolution and field of view (FoV) (Wildenschild & Sheppard, 2013; Wildenschild et al, 2002). This is a pressing issue since resolution needs to be high enough to detect micrometer‐sized pore features that impact flow while obtaining a FoV that is representative (Singh et al, 2019).…”