Advertisement

Pore-Network Model for Geo-Materials

  • Liming Hu
  • Haohao Guo
  • Pengwei Zhang
  • Dongming Yan
Conference paper

Abstract

Pore-network model is a convenient tool to investigate the micromechanics of seepage in porous media. Geo-materials are typical porous media, including the different types of soils and rocks from rock-fill with mm-scale pores with high connectivity to gas shale with nm-scale pores and little connectivity. Based on the 2D image from CT or micro-CT technology, the 3D image of soil aggregates/rock matrix and pore structures for different types of geo-materials were obtained by the advance computational graphics technology. The pore size distribution and connectivity were derived from the developed 3D model, which agreed well with the experiment result. The seepage process was also simulated numerically via the developed micro-mechanics seepage model, and the pore-scale phenomena was revealed such as preferential flow. The hydraulic conductivities for various types of geo-materials from numerical simulation agreed well with the laboratory testing data, demonstrating the potential capability of pore-network model in hydraulic properties study for geo-materials.

Keywords

Porous materials CT images reconstruction Pore-network model 

References

  1. 1.
    Zhou, H., et al.: Modeling research on the response of geoelectric fields in a porous media seepage process. J. Geophys. Eng. 14(2), 408–416 (2017)CrossRefGoogle Scholar
  2. 2.
    Tao, Y.: Seepage and stability analysis of coarse grained soil embankment slope under the condition of rainfall. Changsha University of Science and Technology, Changsha (2013)Google Scholar
  3. 3.
    Vicent, V., et al.: A new method developed to characterize the 3D microstructure of frozen apple using X-ray micro-CT. J. Food Eng. 212, 154–164 (2017)CrossRefGoogle Scholar
  4. 4.
    Qin, Y., et al.: A quasi real-time approach to investigating the damage and fracture process in plain concrete by X-Ray tomography. J. Civ. Eng. Manage. 22(6), 792–799 (2016)Google Scholar
  5. 5.
    Thali, M.J., et al.: VIRTOPSY - Scientific documentation, reconstruction and animation in forensic: individual and real 3D data based geo-metric approach including optical body/object surface and radiological CT/MRI scanning. J. Forensic Sci. 50(2), 428–442 (2005)CrossRefGoogle Scholar
  6. 6.
    Song, W., et al.: Assessing relative contributions of transport mechanisms and real gas properties to gas flow in nanoscale organic pores in shales by pore network modelling. Int. J. Heat Mass Transf. 113, 524–537 (2017)CrossRefGoogle Scholar
  7. 7.
    Gao, S., et al.: Two methods for pore network of porous media. Int. J. Numer. Anal. Meth. Geomech. 36(18), 1954–1970 (2012)CrossRefGoogle Scholar
  8. 8.
    Shuangli, T.: New advances of multislice spiral computed tomography. CT Theory Appl. 14(4), 50–53 (2005)Google Scholar
  9. 9.
    Li, X.: A research for reprocessing the data of rock and soil material CT text for the degree of master of engineering. Changjiang River Scientific Research Institute, Wuhan (2012)Google Scholar
  10. 10.
    Li, C., et al.: 3D mesh generation for soil-rock mixture based on CT scanning. Rock Soil Mech. 35(9), 2731–2736 (2014)Google Scholar
  11. 11.
    Jiang, J., et al.: CT triaxial rheological test on coarse-grained soils. Rock Soil Mech. 35(9), 2507–2514 (2014)Google Scholar
  12. 12.
    Cheng, Z., et al.: Application of CT technology in geotechnical mechanics. J. Yangtze River Sci. Res. Inst. 28(3), 33–38 (2011)Google Scholar
  13. 13.
    Sun, H., et al.: 3D identification and analysis of fracture and damage in soil-rock mixtures based on CT image processing. J. China Coal Soc. 39(3), 452–459 (2014)Google Scholar
  14. 14.
    Cheng, Y., et al.: Three-dimensional reconstruction of soil pore structure and prediction of soil hydraulic properties based on CT images. Trans. Chin. Soc. Agric. Eng. 28(22), 115–122 (2012)Google Scholar
  15. 15.
    Lochmann, K., et al.: Statistical analysis of random sphere packings with variable radius distribution. Solid State Sci. 8(12), 1397–1413 (2006)CrossRefGoogle Scholar
  16. 16.
    Hongqin, D.: The Research on Sphere Random Packings and Packing Structure. Soochow University, Suzhou (2011)Google Scholar
  17. 17.
    Bryant, S.L., et al.: Network model evaluation of permeability and spatial correlation in a real random sphere packing. Transp. Porous Media 11(1), 53–70 (1993)CrossRefGoogle Scholar
  18. 18.
    Kantzas, A., Chatzis, I.: Network simulation of relative permeability curves using a bond correlated-site percolation model of pore structure. Chem. Eng. Commun. 69, 191–214 (1988)CrossRefGoogle Scholar
  19. 19.
    Gao, L., Chen, W.: The Application and Prospect of CT. CT Theory Appl. 18(1), 99–109 (2009)Google Scholar
  20. 20.
    Xianchao, W.: Research on Local Reconstruction Algorithm of CT Images. PLA Information Engineering University, Zhengzhou (2013)Google Scholar
  21. 21.
    Wenli, Y.: Research on the Techniques for 3D Reconstruction and Visualization of Slicing Image Sequence of Granular Soil. Huazhong University of Science and Technology, Wuhan (2012)Google Scholar
  22. 22.
    Yang, K., et al.: Fast bilateral filtering using the discrete cosine transform and the recursive method. Optik 126(6), 592–595 (2015)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Kan, G., et al.: Accelerating the SCE-UA global optimization method based on multi-core CPU and many-core GPU. Adv. Meteorol. (2016)Google Scholar
  25. 25.
    Lagarias, J.C., et al.: Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)CrossRefGoogle Scholar
  26. 26.
    Bryant, S., Blunt, M.: Prediction of relative permeability in simple porous-media. Phys. Rev. A 46(4), 2004–2011 (1992)CrossRefGoogle Scholar
  27. 27.
    Wyllie, M.R.J., Gregory, A.R.: Fluid flow through unconsolidated porous aggregates - effect of porosity and particle shape on kozeny-carman constants. Ind. Eng. Chem. 47(7), 1379–1388 (1955)CrossRefGoogle Scholar
  28. 28.
    Chu, C.F., Ng, K.M.: Flow in packed tubes with a small tube to particle diameter ratio. AIChE J. 35(1), 148–158 (1989)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Liming Hu
    • 1
  • Haohao Guo
    • 1
  • Pengwei Zhang
    • 1
  • Dongming Yan
    • 2
  1. 1.Department of Hydraulic EngineeringTsinghua UniversityBeijingChina
  2. 2.National Laboratory of Pattern Recognition (NLPR), Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations