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

Ankita Singh, Klaus Regenauer-Lieb, Stuart D. C. Walsh, Ryan T. Armstrong, Joost J. M. van Griethuysen, Peyman Mostaghimi*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Plain Language Summary Representative elementary volumes or REVs are defined as the smallest volume of the rock sample that encompasses the region of local heterogeneities for the length scale and property being investigated. X-ray micro-computed tomographic (micro-CT) images capture the rock structure as different gray-level intensities. Traditionally, raw or grayscale micro-CT images undergo a series of image processing steps to obtain segmented micro-CT images wherein a label is assigned to pore space and minerals. REVs are then estimated based on properties calculated from these segmented images. While it is preferred that information-rich raw micro-CT images be used for such an analysis, there are limitations on properties that can be calculated. To tackle this challenge, we introduce a novel texture characterization technique that can be directly applied to raw micro-CT images. This approach captures valuable information about gray-level intensities and their connectivity in a 3D space. The statistics then allow us to describe important aspects of the pore spaces that can otherwise only be inferred from their binary equivalent. In addition to this, using this texture characterization technique would allow us to infer REV sizes in a robust and computationally efficient manner.

Original languageEnglish
Article numbere2020GL088594
Number of pages9
JournalGeophysical Research Letters
Issue number15
Publication statusPublished - 16 Aug 2020


  • grayscale REV
  • micro-CT images
  • porous media
  • representative elementary volumes
  • texture characterization
  • FLOW


Dive into the research topics of 'On Representative Elementary Volumes of Grayscale Micro-CT Images of Porous Media'. Together they form a unique fingerprint.

Cite this