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Erdors Basin Yanchang Formation Tight Sandstone 3D Microstructures

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About this Collection

Erdors Basin Yanchang Formation Tight Sandstone 3D Microstructures


Collection of tight sandstone data include CT projection images at 25keV, 35keV and 45keV, CT reconstructed slices, and DCM reconstructed pore and mineral phases distributions in 3D. Additional information and data, such as .dcm data files and animations, are also included. The natural tight sandstone sample used for experiment with diameter and he... more


Geophysics not elsewhere classified


https://doi.org/10.4225/08/532BB4A949D8C


19 Feb 2012


21 Oct 2013


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tight sandstone multi-energy X-ray micro-CT 3D microstructure


X-ray micro-tomography experiments were carried out on the BL13W beamline at the Shanghai Synchrotron radiation Facility (SSRF). The tomography images were acquired at monochromatic beam energies of 25keV, 35keV and 45keV, respectively. The distance between the sample and the CCD detector was set to 40mm. Effective pixel size is 3.7μm. For each CT scans, 720 projection images were recorded during a 180 degree rotation along the vertical axis with a fixed angular displacement of 0.25 degree. The exposure time for each projection image was 1s, 1.5s and 8s at beam energies 25keV, 35keV and 45keV, respectively. In the experiments, dark-field images and flat-field images were also recorded. Each flat-field image was recorded after every 40 sample projection images. At the end of the experiments, dark-view fields were recorded. Due to the temporal and spatial variations of synchrotron radiation light, a least squares polynomial curve fitting was used to compensate the X-ray light intensity variations in the vertical direction for each flat-field and projection image, and then these corrected image data were pre-processed and reconstructed using X-TRACT software. Pre-processing includes background correction, normalization, phase-retrieval and ring filtering. After pre-processing the data was reconstructed using a standard FBP parallel-beam algorithm. The CT-slices have been analysed using the DCM least-square segmentation module. The size of the selected area is 600 x 600 x 700. The offset position in each CT-Slice is from 200 to 800, both for X and Y directions. The input density of pyrite and calcite are 4.9g/cm3 and 2.71g/cm3, respectively. The mixed density of quartz and albite is 2.635g/cm3, mixture ratio is 1:1. By averaging over the 700 slices, the calculated void volume fraction (porosity) is 5.292%, the volume fraction for the pyrite is 0.577%, for the calcite is 22.300%, and for the mixture of quartz and albite is 71.800%.


Rukai Zhu , Bin Bai, PetrolChina, sample preparation; Huihua Kong, NUC, Haipeng Wang, SXU, X-ray CT image acquisition; Huihua Kong, NUC, Sam Yang, Sherry Mayo, CMSE, CT reconstruction; Sam Yang, CMSE, Ruru Li, Jinxiao Pan, NUC, compositional microstructure calculation


Creative Commons Attribution 3.0 Unported Licence


CSIRO (Australia), Shanghai Synchrotron Radiation Facility (SSRF) (China)


Li, Ruru; Kong, Huihua; Yang, Sam; Mayo, Sherry; Zhu, Rukai; Bai, Bin; Pan, Jinxiao; Wang, Haipeng (2013): Erdors Basin Yanchang Formation Tight Sandstone 3D Microstructures. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/532BB4A949D8C


All Rights (including copyright) CSIRO Australia 2013.


The metadata and files (if any) are available to the public.

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Location Details

37°0′0″ N


36°0′0″ N


110°0′0″ E


109°0′0″ E


WGS84


About this Project

Data Constrained Materials C2011/6428


The objective of the project is to develop a data-constrained modelling methodology and software implementation to advance CSIRO's capability in quantitative 3D characterisation of materials microstructures and their bulk properties. Benchmark case studies are conducted in collaboration with CSIRO impact Flagships and Themes to elevate the impact C... more


Sam Yang


Data-constrained modelling (DCM)


Data-constrained characterisation of materials microstructures and performance properties by incorporating quantitative multi-energy X-ray CT in a generic statistical mechanical modelling framework.


CaseStudy


Ruru Li


Huihua Kong


Sam Yang


Sherry Mayo


Rukai Zhu


Bin Bai


Jinxiao Pan


Haipeng Wang


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