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Automatic Data Reduction and Quantification of X-Ray Computed Tomography Images of Sedimentary Cores: Method and Illustration

Authors :
Philippe Gaillot
Mathieu J. Duchesne
Peter Blum
Source :
Open Journal of Geology. 10:874-899
Publication Year :
2020
Publisher :
Scientific Research Publishing, Inc., 2020.

Abstract

This paper presents a procedure from which information contained in 3-Dimensional single energy X-ray computed tomography (XR-CT) images of sedimentary rocks is converted into sub-mm scale resolution core scalar and core image logs. This new data provide a quantitative and compact (data volume reduction of ~90%) description of the XR-CT images. Density-related outputs are calibrated through automatic integration with continuous digital visual core description (VCD) and discrete moisture and density (MAD) property index measurements of selected samples. After lithology-based calibration of the X-ray attenuation coefficients into density values, quantitative displays include: 1) histogram of the distribution of density values and its related statistical parameters, 2) radial and angular distributions of core density values, 3) volume, average density and mass contributions of three core fractions defined by density thresholds corresponding to voids or vugs (VV, density ≤ ~1 g•cm−3), and a break in the histogram of distribution of the density values showing the limit between the damaged (DM) and non-damaged (ND) fractions of the core material, and so, 4) providing a sub-mm scale bulk density core log free of any drilling disturbance. The procedure is illustrated on data from the 365 m deep Hole C9001C drilled off-shore Shimokita (northeast coast of Honshu, Japan). Usage of the outputs include: 1) derivation of sub-mm scale porosity core log, 2) correction of volume sensitive measurements in case of poor core quality and partially filled core liner, and 3) seismic modeling and well ties.

Details

ISSN :
21617589 and 21617570
Volume :
10
Database :
OpenAIRE
Journal :
Open Journal of Geology
Accession number :
edsair.doi...........47c2bbaa964177260f5b048b0bc371ea
Full Text :
https://doi.org/10.4236/ojg.2020.108040