Analyses of representative elementary volume for coal using X-ray μ-CT and FIB-SEM and its application in permeability predication model

Hao Wu, Yanbin Yao (Corresponding Author), Yingfang Zhou, Feng Qiu

Research output: Contribution to journalArticle

Abstract

The representative elemental volume (REV) study provides a bridge between macro and micro properties’ research, which is critical for understanding and predicting the heterogeneous properties of a porous media. Permeability, one of the essential properties, dominates the capability of fluid flow in porous media, which is scale dependent and thus one of the most rationale way to predict macro scale permeability is to calculate the permeability at REV. Porosity is the most common parameter to determine REV, however, the porosity based REV works less satisfactory for complex pore system. In this work, we determined the REV based on fractal dimension, which is a fundamental parameter to characterize the complex pore network, and then the relation between fractal dimension and sample size was investigated extensively. We then determined and compared the REV from the porosity and fractal dimension that calculated from various sample sizes. Our results reveal that the relationship between fractal dimension-based REV and porosity-based REV can be classified as four cases, and the most common case is porosity declines if the domain is larger than fractal dimension-based REV size. The relation discussed above can be applied to existing fractal permeability models to predict the permeability at different scales.
Original languageEnglish
Article number115563
Number of pages9
JournalFuel
Volume254
Early online date12 Jun 2019
DOIs
Publication statusE-pub ahead of print - 12 Jun 2019

Fingerprint

Coal
Fractal dimension
Porosity
X rays
Scanning electron microscopy
Macros
Porous materials
Fractals
Flow of fluids

Keywords

  • coalbed methane
  • 3D pore structure
  • REV
  • fractal dimension
  • permeability
  • MICROTOMOGRAPHY
  • NANOTOMOGRAPHY
  • Permeability
  • PORES
  • NMR
  • Fractal dimension
  • FRACTAL ANALYSIS
  • PARTICULATE SYSTEMS
  • Coalbed methane

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Chemical Engineering(all)
  • Fuel Technology
  • Organic Chemistry

Cite this

Analyses of representative elementary volume for coal using X-ray μ-CT and FIB-SEM and its application in permeability predication model. / Wu, Hao; Yao, Yanbin (Corresponding Author); Zhou, Yingfang; Qiu, Feng.

In: Fuel, Vol. 254, 115563, 15.10.2019.

Research output: Contribution to journalArticle

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abstract = "The representative elemental volume (REV) study provides a bridge between macro and micro properties’ research, which is critical for understanding and predicting the heterogeneous properties of a porous media. Permeability, one of the essential properties, dominates the capability of fluid flow in porous media, which is scale dependent and thus one of the most rationale way to predict macro scale permeability is to calculate the permeability at REV. Porosity is the most common parameter to determine REV, however, the porosity based REV works less satisfactory for complex pore system. In this work, we determined the REV based on fractal dimension, which is a fundamental parameter to characterize the complex pore network, and then the relation between fractal dimension and sample size was investigated extensively. We then determined and compared the REV from the porosity and fractal dimension that calculated from various sample sizes. Our results reveal that the relationship between fractal dimension-based REV and porosity-based REV can be classified as four cases, and the most common case is porosity declines if the domain is larger than fractal dimension-based REV size. The relation discussed above can be applied to existing fractal permeability models to predict the permeability at different scales.",
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note = "We acknowledge financial support from the National Natural Science Foundation of China (41872123; 41830427), the Petro China Innovation Foundation (2018D-5007-0101), the Key research and development project of Xinjiang Uygur Autonomous Region (2017B03019-1), the Royal Society Edinburgh through the international cost share scheme and National Natural Science Foundation China (NSFC 41711530129).",
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