Improved estimates of percolation and anisotropic permeability from 3-D X-ray mircotomography using stochastic analyses and visualization

Jie Liu, Klaus Regenauer-Lieb, C. Hines, K. Liu, O. Gaede, A. Squelch

    Research output: Contribution to journalArticle

    35 Citations (Scopus)

    Abstract

    X-ray microtomography (micro-CT) with micron resolution enables new ways of characterizing microstructures and opens pathways for forward calculations of multiscale rock properties. A quantitative characterization of the microstructure is the first step in this challenge. We developed a new approach to extract scale-dependent characteristics of porosity, percolation, and anisotropic permeability from 3-D microstructural models of rocks. The Hoshen-Kopelman algorithm of percolation theory is employed for a standard percolation analysis. The anisotropy of permeability is calculated by means of the star volume distribution approach. The local porosity distribution and local percolation probability are obtained by using the local porosity theory. Additionally, the local anisotropy distribution is defined and analyzed through two empirical probability density functions, the isotropy index and the elongation index. For such a high-resolution data set, the typical data sizes of the CT images are on the order of gigabytes to tens of gigabytes; thus an extremely large number of calculations are required. To resolve this large memory problem parallelization in OpenMP was used to optimally harness the shared memory infrastructure on cache coherent Non-Uniform Memory Access architecture machines such as the iVEC SGI Altix 3700Bx2 Supercomputer. We see adequate visualization of the results as an important element in this first pioneering study.
    Original languageEnglish
    Pages (from-to)Q05010
    JournalGeochemistry Geophysics Geosystems
    Volume10
    DOIs
    Publication statusPublished - 2009

    Fingerprint Dive into the research topics of 'Improved estimates of percolation and anisotropic permeability from 3-D X-ray mircotomography using stochastic analyses and visualization'. Together they form a unique fingerprint.

    Cite this