Data-constrained characterization of sandstone microstructures with multi-energy X-ray CT
| dc.contributor.author | Yang, Yushuang | |
| dc.contributor.author | Tulloh, Andrew M | |
| dc.contributor.author | Chen, Fangfiona | |
| dc.contributor.author | Liu, Keyu | |
| dc.contributor.author | Clennell, Ben M | |
| dc.contributor.author | Taylor, John | |
| dc.date.accessioned | 2018-11-29T22:55:49Z | |
| dc.date.available | 2018-11-29T22:55:49Z | |
| dc.date.issued | 2013 | |
| dc.date.updated | 2018-11-29T08:08:30Z | |
| dc.description.abstract | A 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.mimetype | application/pdf | en_AU |
| dc.identifier.issn | 1742-6588 | |
| dc.identifier.uri | http://hdl.handle.net/1885/153296 | |
| dc.publisher | American Institute of Physics | |
| dc.source | Journal of Physics: Conference Series (Print) | |
| dc.title | Data-constrained characterization of sandstone microstructures with multi-energy X-ray CT | |
| dc.type | Journal article | |
| dcterms.accessRights | Open Access | en_AU |
| local.bibliographicCitation.issue | 1 | |
| local.contributor.affiliation | Yang, Yushuang, CSIRO Materials Science & Engineering | |
| local.contributor.affiliation | Tulloh, Andrew M, CSIRO Materials Science & Engineering | |
| local.contributor.affiliation | Chen, Fangfiona, CSIRO Materials Science & Engineering | |
| local.contributor.affiliation | Liu, Keyu, CSIRO Earth Science and Resource Engineering | |
| local.contributor.affiliation | Clennell, Ben M, CSIRO Earth Science and Resource Engineering | |
| local.contributor.affiliation | Taylor, John, College of Engineering and Computer Science, ANU | |
| local.contributor.authoruid | Taylor, John, u1486570 | |
| local.description.notes | Imported from ARIES | |
| local.description.refereed | Yes | |
| local.identifier.absfor | 080104 - Computer Vision | |
| local.identifier.ariespublication | a383154xPUB7853 | |
| local.identifier.citationvolume | 463 | |
| local.identifier.doi | 10.1088/1742-6596/463/1/012048 | |
| local.identifier.scopusID | 2-s2.0-84891327593 | |
| local.identifier.thomsonID | 000327949000048 | |
| local.type.status | Published Version |
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