Quantifying the heterogeneity of shale through statistical combination of imaging across scales
Abstract
Shale is a highly heterogeneous material across multiple scales. A typical shale consists of nanometer-scale pores and minerals mixed with macro-scale fractures and particles of varying size. High-resolution imaging is crucial for characterizing the composition and microstructure of this rock. Yet, it is generally not feasible to image a large sample of shale at a high resolution over a large field of view (FOV), thus limiting a full characterization of the microstructure of this material. We present a stochastic framework based on multiple-point statistics that uses high-resolution training images to enhance low-resolution images obtained over a large FOV. Here, we demonstrate the approach using X-ray micro-tomography images of organic-rich Woodford shale obtained at two different resolutions and FOV. Results show that the proposed technique can generate realistic high-resolution images of the microstructure of shale over a large FOV.
- Authors:
-
- Stanford Univ., CA (United States)
- Publication Date:
- Research Org.:
- Stanford Univ., CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division; National Science Foundation (NSF)
- OSTI Identifier:
- 1597173
- Grant/Contract Number:
- FG02-03ER15454; CMMI-1462231; ECCS-1542152
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Acta Geotechnica
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 6; Journal ID: ISSN 1861-1125
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; Multi-scale imaging; Multiple point statistics; Porous media; Shale; Stochastic simulation; X-ray micro-CT
Citation Formats
Semnani, Shabnam J., and Borja, Ronaldo I. Quantifying the heterogeneity of shale through statistical combination of imaging across scales. United States: N. p., 2017.
Web. doi:10.1007/s11440-017-0576-7.
Semnani, Shabnam J., & Borja, Ronaldo I. Quantifying the heterogeneity of shale through statistical combination of imaging across scales. United States. https://doi.org/10.1007/s11440-017-0576-7
Semnani, Shabnam J., and Borja, Ronaldo I. Tue .
"Quantifying the heterogeneity of shale through statistical combination of imaging across scales". United States. https://doi.org/10.1007/s11440-017-0576-7. https://www.osti.gov/servlets/purl/1597173.
@article{osti_1597173,
title = {Quantifying the heterogeneity of shale through statistical combination of imaging across scales},
author = {Semnani, Shabnam J. and Borja, Ronaldo I.},
abstractNote = {Shale is a highly heterogeneous material across multiple scales. A typical shale consists of nanometer-scale pores and minerals mixed with macro-scale fractures and particles of varying size. High-resolution imaging is crucial for characterizing the composition and microstructure of this rock. Yet, it is generally not feasible to image a large sample of shale at a high resolution over a large field of view (FOV), thus limiting a full characterization of the microstructure of this material. We present a stochastic framework based on multiple-point statistics that uses high-resolution training images to enhance low-resolution images obtained over a large FOV. Here, we demonstrate the approach using X-ray micro-tomography images of organic-rich Woodford shale obtained at two different resolutions and FOV. Results show that the proposed technique can generate realistic high-resolution images of the microstructure of shale over a large FOV.},
doi = {10.1007/s11440-017-0576-7},
journal = {Acta Geotechnica},
number = 6,
volume = 12,
place = {United States},
year = {2017},
month = {8}
}
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