Time-Lapsed Visualization and Characterization of Shale Diffusion Properties Using 4D X-ray Microcomputed Tomography
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Zhang, Yulai; Mostaghimi, Peyman; Fogden, Andrew; Sheppard, Adrian; Arena, Alessio; Middleton, Jill; Armstrong, Ryan
Description
Diffusion is an important mass transport mechanism in shale matrix, which usually has pore sizes ranging from molecular dimensions to micrometers. Better characterization of the diffusion properties is helpful in understanding the multiphysical mass transport process in shale. We present a method for measuring the local effective diffusivity of shale core plugs using four-dimensional (4D) X-ray microcomputed tomography (micro-CT). Liquid-liquid diffusion of X-ray-opaque CH2I2 from a Permian...[Show more]
dc.contributor.author | Zhang, Yulai | |
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dc.contributor.author | Mostaghimi, Peyman | |
dc.contributor.author | Fogden, Andrew | |
dc.contributor.author | Sheppard, Adrian | |
dc.contributor.author | Arena, Alessio | |
dc.contributor.author | Middleton, Jill | |
dc.contributor.author | Armstrong, Ryan | |
dc.date.accessioned | 2019-05-13T01:33:36Z | |
dc.identifier.issn | 0887-0624 | |
dc.identifier.uri | http://hdl.handle.net/1885/161653 | |
dc.description.abstract | Diffusion is an important mass transport mechanism in shale matrix, which usually has pore sizes ranging from molecular dimensions to micrometers. Better characterization of the diffusion properties is helpful in understanding the multiphysical mass transport process in shale. We present a method for measuring the local effective diffusivity of shale core plugs using four-dimensional (4D) X-ray microcomputed tomography (micro-CT). Liquid-liquid diffusion of X-ray-opaque CH2I2 from a Permian Basin shale core plug into the surrounding X-ray-transparent toluene is monitored by 4D micro-CT imaging. The time-sequenced diffusion tomograms enable 4D visualization of the dynamic process. Local directional effective diffusivities are measured numerically from the micro-CT data using a mathematical method. The measured data are analyzed in relation to compositional variations of the sample. The Dykstra-Parsons coefficient is used to quantify the degree of heterogeneity of the measured data at the subcore scale. We find that the diffusion in the Permian Basin subplug is uneven and influenced by matrix heterogeneities. Dense materials (e.g., pyrite) have low porosity and low horizontal effective diffusivity of around 10-15 m2/s or below; light materials (e.g., fossil) have high porosity and high horizontal effective diffusivity of around 10-14 m2/s or above. Compositional variation of the sample leads to porosity and mass transport property changes. 4D imaging and local diffusivity measurements identify the true heterogeneity of the shale sample, which is advantageous over static imaging. The measured local effective diffusivity enables us to infer smaller-scale characteristics and thus provides a means to relate microscale shale rock structure to macroscale transport properties. | |
dc.description.sponsorship | This research project was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. We acknowledge funding from the member companies of the ANU/UNSW Digicore Research Consortium. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_AU | |
dc.publisher | American Chemical Society | |
dc.rights | © 2018 American Chemical Society | |
dc.source | Energy and Fuels | |
dc.title | Time-Lapsed Visualization and Characterization of Shale Diffusion Properties Using 4D X-ray Microcomputed Tomography | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 32 | |
dc.date.issued | 2018 | |
local.identifier.absfor | 091405 - Mining Engineering | |
local.identifier.absfor | 090499 - Chemical Engineering not elsewhere classified | |
local.identifier.absfor | 030699 - Physical Chemistry not elsewhere classified | |
local.identifier.ariespublication | u6048437xPUB542 | |
local.publisher.url | https://pubs.acs.org/ | |
local.type.status | Published Version | |
local.contributor.affiliation | Zhang, Yulai, University of New South Wales | |
local.contributor.affiliation | Mostaghimi, Peyman, The University of New South Wales | |
local.contributor.affiliation | Fogden, Andrew, FEI Oil & Gas | |
local.contributor.affiliation | Sheppard, Adrian, College of Science, ANU | |
local.contributor.affiliation | Arena, Alessio, FEI Oil & Gas | |
local.contributor.affiliation | Middleton, Jill, College of Science, ANU | |
local.contributor.affiliation | Armstrong, Ryan, The University of New South Wales | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 3 | |
local.bibliographicCitation.startpage | 2889 | |
local.bibliographicCitation.lastpage | 2900 | |
local.identifier.doi | 10.1021/acs.energyfuels.7b03191 | |
local.identifier.absseo | 280120 - Expanding knowledge in the physical sciences | |
dc.date.updated | 2023-01-08T07:17:39Z | |
local.identifier.scopusID | 2-s2.0-85044152499 | |
local.identifier.thomsonID | WOS:000428003800026 | |
Collections | ANU Research Publications |
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