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Statistical model for dispersion in a 2D glass micromodel

Ghazanfari, M. H ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.2118/113343-PA
  3. Publisher: 2010
  4. Abstract:
  5. Microscopic visualization of a porous medium can provide valuable information to enhance understanding of pore-scale transport phenomena. In this work, a novel and unique approach is provided to combine experimentally measured pore-size distribution with theoretical statistical analysis to predict longitudinal and transverse dispersion coefficients. The approach presented can be easily extended to predict other fluid-flow parameters through porous media, such as permeability, and capillary pressure. Here, a micromodel is considered as a porous medium. The grains and pores of the micromodel are nonuniform in size, shape, and distribution. The pore-size distribution, as well as pore-length distribution, was extracted by applying an image analysis technique. A 2D random network model of the micromodel has been constructed for which the nonuniformity is considered by assigning measured distribution functions. The random particle method was applied for correlating and predicting dispersion coefficients on the basis of probabilistic approaches. Statistical derivations result in a new functional dependence for the longitudinal and transverse dispersion coefficients in terms of pore velocity and ensemble averages. Prediction from the derived model, for both longitudinal and transverse dispersion, is in agreement with the experimental data. Despite the simplicity of the proposed network model, its reasonable prediction provides some confidence that it can be considered as a reasonable approximation of the complex nature of porous media. Copyright
  6. Keywords:
  7. Capillary pressures ; Complex nature ; Dispersion coefficient ; Ensemble averages ; Experimental data ; Flow parameters ; Functional dependence ; Image analysis techniques ; Length distributions ; Microscopic visualization ; Network models ; Nonuniform ; Nonuniformity ; Pore velocity ; Porous Media ; Porous medium ; Probabilistic approaches ; Random network models ; Random particles ; Statistical analysis ; Statistical models ; Transport phenomena ; Transverse dispersion ; Distribution functions ; Forecasting ; Image analysis ; Pore size ; Porous materials ; Size distribution ; Visualization ; Dispersions
  8. Source: SPE Journal ; Volume 15, Issue 2 , 2010 , Pages 301-312 ; 1086055X (ISSN)
  9. URL: https://www.onepetro.org/journal-paper/SPE-113343-PA