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Pore to pore validation of pore network modelling against micromodel experiment results

Computational Geosciences, ISSN: 1573-1499, Vol: 21, Issue: 5-6, Page: 849-862
2017
  • 17
    Citations
  • 0
    Usage
  • 52
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    17
    • Citation Indexes
      17
  • Captures
    52

Article Description

Pore network modelling (PNM) has been widely used to study the multiphase flow and transport in porous media. Although a number of recent papers discussed the PNM validation on core-scale parameters such as permeability, relative permeability and capillary pressure; quantitative predictive potential of PNM on pore by pore basis has rarely been studied. The aim of this paper is to present a direct comparison between PNM simulations and corresponding micro-model experiments at the same scale and the same geometry. A number of well-defined and constrained two-phase flow in porous medium experimental scenarios were utilized to validate the physics solving part in PNM (filling rules, capillary and viscous pressure). This work validates that a dynamic pore network flow solver can predict two-phase flow displacements for these experiments for drainage situations at both pore and plug scales. A glass-etched micro-model is used to quantify the accuracy of a dynamic PNM solver on pore and core levels. Two-phase drainage micro fluidic experiments at different flow conditions are performed on micro-models. PNM simulations are performed on the same pattern and flow conditions as used in micro-model experiments. The two-phase distribution extracted from experiment images is registered onto rsults of PNM simulations for direct pore to pore comparison. Pore-scale matching level is found at around 75 % for all three test cases. The matching level of core-scale parameters such as S and oil-phase permeability varies from case to case; the relative error to micro-model experiment measurements varies from 15 to 60 %. Possible reasons leading to discrepancies on core-scale parameters are discussed: missing considerations during validation of the combination of uncertainty in both simulator input parameters and experiments are seen as the principal factors.

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