Percolation Models for Transport in Porous Media

Selyakov, V. I.; Kadet, V. V.

doi:10.1007/978-94-015-8626-9

Cited by 38 publications

(16 citation statements)

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“…Formulae for the flux reduction and accessibility factors ( (14) and (19)) 263 can be derived for regular pore networks using effective medium or perco-264 lation theories (Sharma and Yortsos, 1987b,c;Seljakov and Kadet, 1996). 265…”

mentioning

confidence: 99%

A Stochastic Model for Particulate Suspension Flow in Porous Media

Santos¹,

Bedrikovetsky²

2006

Transp Porous Med

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Abstract. A population balance model for a particulate suspension transport with size 6 exclusion capture of particles by porous rock is derived. The model accounts for particle 7 flux reduction and pore space accessibility due to restriction for large particles to move 8 through smaller pores -a particle is captured by a smaller pore and passes through a 9 larger pore. Analytical solutions are obtained for a uniform pore size medium, and also 10 for a medium with small pore size variation. For both cases, the equations for averaged 11 concentrations significantly differ from the classical deep bed filtration model. 12

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confidence: 99%

A Stochastic Model for Particulate Suspension Flow in Porous Media

Santos¹,

Bedrikovetsky²

2006

Transp Porous Med

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“…Geometry of the infinite cluster of accessible pores is shown in Fig. 9; here R is the correlation radius of the accessible sub-network [38][39][40][41]. The larger is a particle the smaller fraction of the overall network is accessible to it, and the larger is the correlation radius R of the accessible sub-network.…”

Section: Analysis Of the Numerical Resultsmentioning

confidence: 99%

“…The reason is that the two-dimensional network model is used for computations. The percolation threshold for a 2D network is equal to 0.5, so there is no network connectivity for large particles with the percolation probability below 0.5 [38][39][40][41]. The size of the largest particle that has a chance to reach the network outlet without being captured is equal to the mean value of f H distribution, which corresponds to j=1.…”

Section: Modelling Of Deep Bed Filtrationmentioning

confidence: 99%

Improved population balance model for straining-dominant deep bed filtration using network calculations

Yuan

You

Shapiro

et al. 2013

Chemical Engineering Journal

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“…Therefore, percolation model can be applied to study filtration and obtain accurate results [16][17][18][19] . Percolation model can also be used to predict the properties of medium [20][21][22] and is associated with network modeling of transport in porous media. Hao et al [23] applied percolation theory and random walk in a 2D square network to study staining-dominate DBF; this study proposed two particle capture mechanisms and two power-laws that describe the relationship between the fractions of flow through the small pores and the filtration coefficient.…”

Section: Introductionmentioning

confidence: 99%

The prediction of particle size distribution for the effluent polydisperse particle in staining-dominate deep bed filtration process

Ding¹,

Li²,

Luo³

et al. 2017

Proceedings of the 2017 6th International Conference on Energy and Environmental Protection (ICEEP 2017)

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Abstract.A power law relation between the filtration coefficients of straining and flux through small pores based on percolation theory has been reported. It was a monodisperse particle filtration theory, while the actual colloidal suspension is polydisperse particles. In this study, a formula of particle size distribution for the effluent polydisperse particle in staining-dominate deep bed filtration (DBF) process is proposed based on preliminary results of the monodisperse filtration theory. In order to validate this formula, a numerical staining-dominate DBF model is established to obtain particle size distribution for the effluent polydisperse particle, using the parameters of experimental data set to configure the simulation conditions. The numerical simulation is consistent with the predicted results calculated from the formula. It's indicated that the formula applied for the prediction of particle size distribution for the effluent polydisperse particle in staining-dominate DBF process is relatively accurate and effective.

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Percolation Models for Transport in Porous Media

Cited by 38 publications

References 0 publications

A Stochastic Model for Particulate Suspension Flow in Porous Media

A Stochastic Model for Particulate Suspension Flow in Porous Media

Improved population balance model for straining-dominant deep bed filtration using network calculations

The prediction of particle size distribution for the effluent polydisperse particle in staining-dominate deep bed filtration process

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