Cleat structure analysis and permeability simulation of coal samples based on micro-computed tomography (micro-CT) and scan electron microscopy (SEM) technology

Alexandra Roslin, Dubravka Pokrajac, Yingfang Zhou* (Corresponding Author)

*Corresponding author for this work

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59 Citations (Scopus)
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Abstract

Coal has been playing an important role as a valuable source of energy for many years. In turn, gas production from coal reservoirs is a modern development and coal bed methane (CBM), also known as coal seam gas (CSG), is attracting global attention due to its wide occurrence and benefits for the environment as opposed to the conventional energy sources. Developing coal bed methane reservoirs requires better understanding of the flow behaviours of gas and liquids in cleats and analysis of possible contribution of pores to the flow. This paper describes the implementation of micro computed tomography (micro-CT) and scan electron microscopy (SEM) techniques for analysis of coal samples. Intermediate rank coal samples used in this study were collected from Southern Qinshui Basin (China). In the course of the described research, coal samples were scanned, processed and segmented to study the cleat spacing and permeability. Due to the partial volume effect, the resolution of cleats needed improvement which was achieved by subvoxel processing using a novel algorithm as explained in detail in the paper. Permeability was obtained through simulation of one phase flow using Lattice Boltzmann method (LBM). The results show that the simulated permeability is comparable to the analytical approximation. The subvoxel processing has proved an effective method of overcoming the partial volume effect for the low resolution micro-CT images.
Original languageEnglish
Article number115579
Number of pages7
JournalFuel
Volume254
Early online date12 Jun 2019
DOIs
Publication statusPublished - 15 Oct 2019

Bibliographical note

This paper utilised opportunistic coal samples and characterisation data as a part of a study into multiphase flow in coal for Southern Qinshui coal basin. The University of Aberdeen School of Engineering and School of Geosciences are thanked for their support. The authors also thank John Still from The University of Aberdeen School of Geosciences for his support regarding SEM data analysis and Amir Golparvar from The University of Aberdeen School of Engineering for his help with Matlab.

Keywords

  • coal bed methane
  • Micro-CT
  • SEM
  • subvoxel algorithm
  • permeability
  • Lattice Botzmann method

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