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Geosciences Journal

, Volume 23, Issue 1, pp 175–188 | Cite as

Three-dimensional digitalization modeling characterization of pores in high-rank coal in the southern Qinshui basin

  • Shiqi Liu
  • Shuxun SangEmail author
  • Jingsheng Ma
  • Xin Wang
  • Yi Du
  • Tian Wang
Article
  • 58 Downloads

Abstract

Pore connectivity is an important property of coal. To explore the connectivity of pore-fractures in terms of macropores and mesopores in high-rank coal, two coal samples collected from the coal seam #3 in the southern Qinshui basin were selected. A pore-fracture network model of high-rank coal on the nanometer (10–100 nm) to micrometer (0.1–10 μm) scale is constructed, and key parameters are extracted using the 3D (three-dimensional) digital spatial characterization based on 3D scanning with FIB-SEM (Focused Ion Beam Scanning Electron Microscopy). Then, the connectivity of the pore-fractures and the contribution of pores with different genetic types to the connectivity of the high-rank coal are confirmed. The results show that the pores and throats of high-rank coal in coal seam #3 in the southern Qinshui basin are very narrow, with predominant mesopores < 50 nm in width. The tortuosity of the coal samples is low, and the cross-section is predominantly square and triangular in shape, which means that the capillary resistance is small. The connectivity of the pores is poor, and mesopores play an important role in the pore connectivity. Linear differential shrinkage pores are the main connected pores on the nanometer scale and communicate with irregularly rounded and elliptic differential shrinkage pores, secondary pores, and mineral pores. The types and contents of the minerals in coals determine the morphological characteristics and degree of development of the differential shrinkage pores, and have an important influence on the pore connectivity in high-rank coal. The content of quartz determines the degree of development of the linear differential shrinkage pores, and is the primary reasons for the differences in the connectivity of the two samples.

Key words

FIB-SEM connectivity pore-fracture network characteristic parameter Qinshui basin 

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References

  1. An, S.Y., Yao, J., Yang, Y.F., Zhang, W.J., Zhao, J.L., and Li, A.F., 2017, The microscale analysis of reverse displacement based on digital core. Journal of Natural Gas Science and Engineering, 48, 138–144.CrossRefGoogle Scholar
  2. Cai, Y.D., Liu, D.M., Pan, Z.J., Yao, Y.B., Li, J.Q., and Qiu, Y.K., 2013, Petrophysical characterization of Chinese coal cores with heat treatment by nuclear magnetic resonance. Fuel, 108, 292–302.CrossRefGoogle Scholar
  3. Clarkson, C.R., Solano, N., Bustin, R.M., Bustin, A.M.M., Chalmers, G.R.L., He, L., Melnichenko, Y.B., Radlinski, A.P., and Blach, T.P., 2013, Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel, 103, 606–616.CrossRefGoogle Scholar
  4. Delerue, J.F. and Perrier, E., 2002, DXSoil, a library for 3D image analysis in soil science. Computers and Geosciences, 28, 1041–1050.CrossRefGoogle Scholar
  5. Gürdal, G. and Yalçin, M.N., 2001, Pore volume and surface area of the Carboniferous coals from the Zonguldak basin (NW Turkey) and their variations with rank and maceral composition. International Journal of Coal Geology, 48, 133–144.CrossRefGoogle Scholar
  6. Herring, A.L., Harper, E.J., Andersson, L., Sheppard, A., Bay, B.K., and Wildenschild, D., 2013, Effect of fluid topology on residual nonwetting phase trapping: implications for geologic CO2 sequestration. Advances in Water Resources, 62, 47–58.CrossRefGoogle Scholar
  7. Hughes, R.G. and Blunt, M.J., 2001, Network modeling of multiphase flow in fractures. Advances in Water Resources, 24, 409–421.CrossRefGoogle Scholar
  8. Ioannidis, M.A. and Chatzis, I., 2000, On the geometry and topology of 3D stochastic porous media. Journal of Colloid and Interface Science, 229, 323–334.CrossRefGoogle Scholar
  9. Karacan, C.Ö. and Okandan, E., 2010a, Adsorption and gas transport in coal microstructure: investigation and evaluation by quantitative X-ray CT imaging. Fuel, 80, 509–520.CrossRefGoogle Scholar
  10. Karacan, C.Ö. and Okandan, E., 2000b, Fracture/cleat analysis of coals from Zonguldak Basin (northwestern Turkey) relative to the potential of coalbed methane production. International Journal of Coal Geology, 44, 109–125.CrossRefGoogle Scholar
  11. Knackstedt, M., Arns, C., Ghous, A., Sakellariou, A., Senden, T., Sheppard, A., Sok, R., Averdunk, H., Pinczewski, V.W., and Padhy, G.S., 2006a, ANU-Digital Collections: 3D imaging and flow characterization of the pore space of carbonate core samples. Letters in Applied Microbiology, 32, 303–306.Google Scholar
  12. Knackstedt, M., Arns, C., Ghous, A., Sakellariou, A., Senden, T., Sheppard, A., Sok, R., Nguyen, V., and Pinczewski, V.W., 2006b, 3D imaging and characterization of the pore space of carbonate core; implications to single and two phase flow properties. 47th Annual Logging Symposium of the Society of Petrophysicists and Well Log Analysts, Washington, Jun. 4–7, p. 122–122.Google Scholar
  13. Knackstedt, M., Arns, C., Saadatfar, M., Senden, T., Sakellariou, A., Sheppard, A., Sok, R., Schrofand, W., and Steininger, H., 2005, Vir tual materials design: properties of cellular solids derived from 3D tomographic images. Advanced Engineering Materials, 7, 238–243.CrossRefGoogle Scholar
  14. Lee, G., Baek, W., and Chang, S.H., 2002, Improved methodology for generation of axial flux shapes in digital core protection systems. Annals of Nuclear Energy, 29, 805–819.CrossRefGoogle Scholar
  15. Lee, G. and Chang, S.H., 2003, Radial basis function networks applied to DNBR calculation in digital core protection systems. Annals of Nuclear Energy, 30, 1561–1572.CrossRefGoogle Scholar
  16. Li, S., Tang, D.Z., Xu, H., and Yang, Z., 2012, Advanced characterization of physical properties of coals with different coal structures by nuclear magnetic resonance and X-ray computed tomography. Computers and Geosciences, 48, 220–227.CrossRefGoogle Scholar
  17. Lindquist, W.B., Venkatarangan, A., Dunsmuir, J., and Wong, T.F., 2000, Pore and throat size distributions measured from synchrotron Xray tomographic images of Fontainebleau sandstones. Journal of Geophysical Research Solid Earth, 105, 21509–21527.CrossRefGoogle Scholar
  18. Liu, S.Q., Sang, S.X., Wang, G., Ma, J.S., Wang, X., Wang, W.F., Du, Y., and Wang, T., 2017, FIB-SEM and X-ray CT characterization of interconnected pores in high-rank coal formed from regional metamorphism. Journal of Petroleum Science and Engineering, 148, 21–31.CrossRefGoogle Scholar
  19. Liu, S.Q., Sang, S.X., Liu, H.H., and Zhu, Q.P., 2015, Growth characteristics and genetic types of pores and fractures in a high-rank coal reservoir of the southern Qinshui basin. Ore Geology Reviews, 64, 140–151.CrossRefGoogle Scholar
  20. Ma, J.S, Couples, G.D., Jiang, Z., and van Dijke, M.I.J., 2014a, A multiscale framework for digital core analysis of gas shale at millimeter scales. Unconventional Resources Technology Conference, Denver, Aug. 25–27, p. 1–8.Google Scholar
  21. Ma, J.S, Sanchez, J.P., Wu, K., Couples, G.D., and Jiang, Z., 2014b, A pore network model for simulating non-ideal gas flow in microand nano-porous materials. Fuel, 116, 498–508.CrossRefGoogle Scholar
  22. Ma, J.S., Zhang, X.X., Jiang, Z.Y., Ostadi, H., Jiang, K., and Chen, R., 2014c, Flow properties of an intact MPL from nano-tomography and pore network modelling. Fuel, 136, 307–315.CrossRefGoogle Scholar
  23. Moore, T.A., 2012, Coalbed methane: a review. International Journal of Coal Geology, 101, 36–81.CrossRefGoogle Scholar
  24. Øren, P. and Bakke, S., 2003, Reconstruction of Berea sandstone and pore-scale modelling of wettability effects. Journal of Petroleum Science and Engineering, 39, 177–199.CrossRefGoogle Scholar
  25. Prodanovic, M., Lindquist, W.B., and Seright, R.S., 2007, 3D imagebased characterization of fluid displacement in a Berea core. Advances in Water Resources, 30, 214–226.CrossRefGoogle Scholar
  26. Silin, D. and Patzek, T., 2006, Pore space morphology analysis using maximal inscribed spheres. Physica A Statistical Mechanics and Its Applications, 371, 336–360.CrossRefGoogle Scholar
  27. Sok, R.M., Knackstedt, M.A., Sheppard, A.P., Pinczewski, W.V., Lindquist, W.B., Venkatarangan, A., and Paterson, L., 2002, Direct and stochastic generation of network models from tomographic images: effect of topology on residual saturations. Transport in Porous Media, 46, 345–371.CrossRefGoogle Scholar
  28. Van Geet, M., Swennen, R., and David, P., 2001, Quantitative coal characterisation by means of microfocus X-ray computer tomography, colour image analysis and back-scattered scanning electron microscopy. International Journal of Coal Geology, 46, 11–25.CrossRefGoogle Scholar
  29. Vincent, L. and Soille, P., 1991, Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583–598.CrossRefGoogle Scholar
  30. Vogel, H.J., 1997, Morphological determination of pore connectivity as a function of pore size using serial sections. European Journal of Soil Science, 48, 365–377.CrossRefGoogle Scholar
  31. Vogel, H.J. and Roth, K., 2001, Quantitative morphology and network representation of soil pore structure. Advances in Water Resources, 24, 233–242.CrossRefGoogle Scholar
  32. Yan, C.H., Whalen, R.T., Beaupré, G.S., Yen, S.Y., and Napel, S., 2000, Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction. IEEE Transactions on Medical Imaging, 19, 1–11.Google Scholar
  33. Yao, J., Wang, C.C., Yang, Y.F., Hu, R.R., and Wang, X., 2013, The construction of carbonate digital rock with hybrid superposition method. Journal of Petroleum Science and Engineering, 110, 263–267.CrossRefGoogle Scholar
  34. Yao, Y.B., Liu, D.M., Cai, Y.D., and Li, J.Q., 2010, Advanced characterization of pores and fractures in coals by nuclear magnetic resonance and X-ray computed tomography. Science China Earth Sciences, 53, 854–862.CrossRefGoogle Scholar
  35. Yao, Y.B., Liu, D.M., Che, Y., Tang, D.Z., Tang, S.H., and Huang, W.H., 2009, Non-destructive characterization of coal samples from China using microfocus X-ray computed tomography. International Journal of Coal Geology, 80, 113–123.CrossRefGoogle Scholar
  36. Zarrouk, S.J. and Moore, T.A., 2009, Preliminary reservoir model of enhanced coalbed methane (ECBM) in a subbituminous coal seam, Huntly Coalfield, New Zealand. International Journal of Coal Geology, 77, 153–161.CrossRefGoogle Scholar
  37. Zhang, S., Tang, S., Tang, D., Pan, Z., and Yang, F., 2010, The characteristics of coal reservoir pores and coal facies in Liulin district, Hedong coal field of China. International Journal of Coal Geology, 81, 117–127.CrossRefGoogle Scholar

Copyright information

© The Association of Korean Geoscience Societies and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shiqi Liu
    • 1
  • Shuxun Sang
    • 1
    • 2
    Email author
  • Jingsheng Ma
    • 3
  • Xin Wang
    • 4
  • Yi Du
    • 2
  • Tian Wang
    • 2
  1. 1.Key Laboratory of Coal-based CO2 Capture and Geological Storage, Jiangsu Province, Low Carbon Energy InstituteChina University of Mining and TechnologyXuzhou, JiangsuChina
  2. 2.Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, School of Mineral Resource and GeoscienceChina University of Mining and TechnologyXuzhouChina
  3. 3.Institute of Petroleum EngineeringHeriot-Watt UniversityEdinburghUK
  4. 4.Institute of Oceanographic InstrumentationShandong Academy of Sciences (SDIOI)QingdaoChina

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