Particle transport in a saturated porous medium: Pore structure effects

Benamar, Ahmed; Ahfir, Nasre-Dine; Wang, Huaqing; Alem, Abdellah

doi:10.1016/j.crte.2007.07.012

Cited by 40 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, if decreasing hydraulic gradient, the effect of dispersion on eroded mass is reduced. This result is in accordance with reported ones (Benamar et al 2007; As regards to increasing Péclet number, cumulative eroded mass quickly reached an asymptotic value. This evolution indicates that Péclet number has a significant influence on the suffusion process only within the range of its low values (Figs.…”

Section: Coupled Effects Of Dispersion Coefficient and Sample Lengthsupporting

confidence: 93%

Modeling of particles migration in porous media: Application to soil suffusion

Ahmed¹,

Boutarfaïa²

2016

Scour and Erosion

View full text Add to dashboard Cite

The suffusion phenomenon occurs when fine soil particles are detached by seepage flow and transported away from the matrix. This process is one of the main causes of failure of hydraulic structures and road embankments. This study aimed to build a numerical model for simulating the particles suffusion within a porous medium. This model combines a flow law and an erosion equation related to the evolution of soil porosity. In addition the dispersion and the deposition kinetics processes of eroded particles were combined with detachability process. The equations describe the evolution of the instantaneous concentration of the fluidized solid, and the variation of eroded mass. Sensitivity analysis allows highlighting the influence of the different parameters on suffusion, particularly that deposition kinetics starts acting only below a threshold of hydraulic gradient and beyond a given sample length. The adjustment results indicate that the suffusion process is strongly related to hydraulic conditions, physical soil characteristics and pore water chemicals. The comparison of numerical results with experimental data from laboratory tests provides a quite good agreement.

show abstract

Section: Coupled Effects Of Dispersion Coefficient and Sample Lengthsupporting

confidence: 93%

Modeling of particles migration in porous media: Application to soil suffusion

Ahmed¹,

Boutarfaïa²

2016

Scour and Erosion

View full text Add to dashboard Cite

show abstract

“…where c is the concentration of nanoparticles, u is the Darcy velocity, φ is the porosity, D is the dispersion tensor, R represents the entrapment and detachment of nanoparticles due to chemical reactions, and q c represents the source or sink term of the nanoparticle flow [15]. By considering the filtration theory, the deposition of nanoparticles is accompanied by fine migration, and it is coupled with the governing balance equation by combining the rate at which the mass is deposited with the mass accumulation term [15,45,46]; hence, we get the following equation:…”

Section: Mathematical Modeling Of Nanoparticle Transport In Anisotropmentioning

confidence: 99%

Mathematical Modeling and Simulation of Nanoparticle-Assisted Enhanced Oil Recovery—A Review

et al. 2019

View full text Add to dashboard Cite

In the last two decades, nanotechnology has flourished due to its vast number of applications in many fields such as drug delivery, oil and gas, and thermal applications, like cooling and air-conditioning. This study focuses on the applications of nanoparticles/nanofluids in the Enhanced Oil Recovery (EOR) process to increase oil recovery efficiency. To understand the nanoparticle-assisted EOR process, the first step is to understand the flow characteristics of nanoparticles in porous media, including entrapment and release in the pores and the behavior of nanoparticles under high temperatures, pressures, and salinity levels and in the presence of external electric and magnetic fields. Also, the process looks at the roles of various pore distributions during their application as EOR agents. The experimental approaches are not only time consuming, but they are also cumbersome and expensive. Hence, the mathematical models could help to facilitate the understanding of the transport and interaction of nanofluids in a reservoir and how such processes can be optimized to get maximum oil recovery and, in turn, reduce the production cost. This paper reviews and critically analyzes the latest developments in mathematical modeling and simulation techniques that have been reported for nanofluid-assisted EOR. One section is dedicated to discussing the challenges ahead, as well as the research gaps in the modeling approach to help the readers to also contribute to further enlightening the modeling nanofluid-assisted EOR process.

show abstract

“…To discretize the PDEs (5) -(6) using MPFA, we first compute the flux of potential. The flux of a phase α through half cell edge S in an interaction volume can be computed by (17) f…”

Section: Multipoint Flux Approximationmentioning

confidence: 99%