Data-constrained characterization of sandstone microstructures with multi-energy X-ray CT

dc.contributor.authorYang, Yushuang
dc.contributor.authorTulloh, Andrew M
dc.contributor.authorChen, Fangfiona
dc.contributor.authorLiu, Keyu
dc.contributor.authorClennell, Ben M
dc.contributor.authorTaylor, John
dc.date.accessioned2018-11-29T22:55:49Z
dc.date.available2018-11-29T22:55:49Z
dc.date.issued2013
dc.date.updated2018-11-29T08:08:30Z
dc.description.abstractA data-constrained non-linear optimization approach has been developed to characterize microscopic distributions of mineral phases and pores in a sandstone sample using X-ray CT data sets acquired at 35keV and 45keV beam energies as constraints. The approach minimizes discrepancy between the expected and measured linear absorption coefficients and maximizes Boltzmann distribution probability. It enables integration of both the 3D X-ray CT data-constraints and global level information, and leads to more accurate predictions of microscopic 3D compositional distributions in material samples. Permeability simulations and comparisons with experimentally measured porosity indicate that DCM characterisation agrees reasonably with experimental observations. However, segmentation of CT images leads to under-estimation of porosity and permeability.
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1742-6588
dc.identifier.urihttp://hdl.handle.net/1885/153296
dc.publisherAmerican Institute of Physics
dc.sourceJournal of Physics: Conference Series (Print)
dc.titleData-constrained characterization of sandstone microstructures with multi-energy X-ray CT
dc.typeJournal article
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue1
local.contributor.affiliationYang, Yushuang, CSIRO Materials Science & Engineering
local.contributor.affiliationTulloh, Andrew M, CSIRO Materials Science & Engineering
local.contributor.affiliationChen, Fangfiona, CSIRO Materials Science & Engineering
local.contributor.affiliationLiu, Keyu, CSIRO Earth Science and Resource Engineering
local.contributor.affiliationClennell, Ben M, CSIRO Earth Science and Resource Engineering
local.contributor.affiliationTaylor, John, College of Engineering and Computer Science, ANU
local.contributor.authoruidTaylor, John, u1486570
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationa383154xPUB7853
local.identifier.citationvolume463
local.identifier.doi10.1088/1742-6596/463/1/012048
local.identifier.scopusID2-s2.0-84891327593
local.identifier.thomsonID000327949000048
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
01_Yang_Data-constrained_2013.pdf
Size:
714.61 KB
Format:
Adobe Portable Document Format