Determination of the throat size distribution of a porous medium as an inverse optimization problem combining pore network modeling and genetic and hill climbing algorithms - Archive ouverte HAL
Journal Articles Physical Review E : Statistical, Nonlinear, and Soft Matter Physics Year : 2021

Determination of the throat size distribution of a porous medium as an inverse optimization problem combining pore network modeling and genetic and hill climbing algorithms

Otman Maalal
  • Function : Author
Marc Prat
René Peinador
  • Function : Author
Didier Lasseux

Abstract

The pore size distribution of a porous medium is often estimated from the retention curve or the invading fluid flow rate curve using simple relationships more or less explicitly based on the consideration that the porous medium is made of a bundle of cylindrical parallel tubes. This type of determination is tested using pore network simulations. Starting from two-or three-dimensional networks, the characteristics of which are known apriori, the estimation of the throat size distribution (TSD) is performed using the standard methods in the case of drainage. Results show a significant discrepancy with the input data. The disagreement is more pronounced when the fluid flow rate curve is employed together with the parallel tubes assumption. The physical origins of these shortcomings are identified. A method, based on pore network simulations combined with a genetic algorithm and the hill climbing algorithm, is then designed, which makes simultaneous use of the nonwetting fluid flow rate curve and the retention curve of the medium. Very significant improvement is achieved in the estimation of the TSD using this procedure.
Fichier principal
Vignette du fichier
Postprint.pdf (3 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03146345 , version 1 (19-02-2021)

Identifiers

Cite

Otman Maalal, Marc Prat, René Peinador, Didier Lasseux. Determination of the throat size distribution of a porous medium as an inverse optimization problem combining pore network modeling and genetic and hill climbing algorithms. Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, 2021, ⟨10.1103/PhysRevE.103.023303⟩. ⟨hal-03146345⟩
67 View
256 Download

Altmetric

Share

More