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Random walk–percolation-based modeling of two-phase flow in porous media: Breakthrough time and net to gross ratio estimation

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  • Ganjeh-Ghazvini, Mostafa
  • Masihi, Mohsen
  • Ghaedi, Mojtaba

Abstract

Fluid flow modeling in porous media has many applications in waste treatment, hydrology and petroleum engineering. In any geological model, flow behavior is controlled by multiple properties. These properties must be known in advance of common flow simulations. When uncertainties are present, deterministic modeling often produces poor results.

Suggested Citation

  • Ganjeh-Ghazvini, Mostafa & Masihi, Mohsen & Ghaedi, Mojtaba, 2014. "Random walk–percolation-based modeling of two-phase flow in porous media: Breakthrough time and net to gross ratio estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 214-221.
    • Handle: RePEc:eee:phsmap:v:406:y:2014:i:c:p:214-221
    DOI: 10.1016/j.physa.2014.03.051
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    References listed on IDEAS

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    1. King, Peter R. & Jr., José S.Andrade & Buldyrev, Sergey V. & Dokholyan, Nikolay & Lee, Youngki & Havlin, Shlomo & Stanley, H.Eugene, 1999. "Predicting oil recovery using percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 266(1), pages 107-114.
    2. Solomon, Sorin & Weisbuch, Gerard & de Arcangelis, Lucilla & Jan, Naeem & Stauffer, Dietrich, 2000. "Social percolation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 277(1), pages 239-247.
    3. King, P.R & Buldyrev, S.V & Dokholyan, N.V & Havlin, S & Lee, Y & Paul, G & Stanley, H.E, 1999. "Applications of statistical physics to the oil industry: predicting oil recovery using percolation theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 274(1), pages 60-66.
    4. King, P.R & Buldyrev, S.V & Dokholyan, N.V & Havlin, S & Lopez, E & Paul, G & Stanley, H.E, 2002. "Using percolation theory to predict oil field performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 103-108.
    5. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    6. Oliveira, C.L.N. & Araújo, A.D. & Lucena, L.S. & Almeida, M.P. & Andrade, J.S., 2012. "Post-breakthrough scaling in reservoir field simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3219-3226.
    7. King, P.R. & Buldyrev, S.V. & Dokholyan, N.V. & Havlin, S. & Lopez, E. & Paul, G. & Stanley, H.E., 2002. "Uncertainty in oil production predicted by percolation theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 306(C), pages 376-380.
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    Cited by:

    1. Ganjeh-Ghazvini, Mostafa & Masihi, Mohsen & Baghalha, Morteza, 2015. "Study of heterogeneity loss in upscaling of geological maps by introducing a cluster-based heterogeneity number," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 1-13.
    2. Huang, Xudong & Yang, Dong & Kang, Zhiqin, 2021. "Impact of pore distribution characteristics on percolation threshold based on site percolation theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    3. Tavagh-Mohammadi, Behnam & Masihi, Mohsen & Ganjeh-Ghazvini, Mostafa, 2016. "Point-to-point connectivity prediction in porous media using percolation theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 304-313.

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