Computation of the Longitudinal and Transverse Dispersion Coefficient in an Adsorbing Porous Medium Using Homogenization

Bruining, Hans; Darwish, Mohamed I.M.; Rijnks, Aiske

doi:10.1007/s11242-011-9875-x

Cited by 19 publications

(19 citation statements)

References 64 publications

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“… Calculated Pe L in a packed‐bed model from numerical simulations B, D, E, F, and G in comparison to 2‐D theoretical data from Edwards et al and Bruining et al and 3‐D experimental data from Raimondi et al Miller and King, and Gunn and Pryce http://wileyonlinelibrary.com.]…”

Section: Resultsmentioning

confidence: 99%

The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

et al. 2012

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in Wiley Online Library (wileyonlinelibrary.com).The longitudinal (D L ) and transverse (D T ) dispersion coefficients in two-dimensional (2-D) randomly packed beds of circular particles in a laminar flow regime are derived. A 2-D discrete system of particles is divided into cells using modified Voronoi diagrams. The relationship between the variation of the stream function and the averaged vorticity is obtained from computational fluid dynamics (CFD) simulations. The whole flow pattern is then obtained by using the principle of energy dissipation rate minimization. The obtained values of D L agree well with 3-D experimental data for all velocities investigated. At very high velocities, D T in 2-D appears to be higher than 3-D experimental data. In addition, the effects of particle-size distributions, packing structure, and porosity on the D L and D T were studied. One result was that an increase in the width of the particle-size distribution resulted in higher values of D L and D T at high velocities.

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Section: Resultsmentioning

confidence: 99%

The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

et al. 2012

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“…The last point saturation obtained from the bulk is compared with the last value of the recovery profile obtained in [8].…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

Gravity-Enhanced Transfer between Fracture and Matrix in Solvent-Based Enhanced Oil Recovery

et al. 2012

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“…Nevertheless, at low Peclet numbers (Pe < 10) both dispersitivies are of the same order of magnitude [75]- [77]. Dispersion can represent small scale movements not captured by the REV used in the mathematical model, but according to Heimsund [28], taking this into account in the numerical model may be troublesome [78] [79].…”

Section: Compositional Modelsmentioning

confidence: 99%

Methods in Oil Recovery Processes and Reservoir Simulation

Druetta¹,

Tesi²,

Persis³

et al. 2016

ACES

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The exploitation of an oil field is a complex and multidisciplinary task, which demands a lot of prior knowledge, time, and money. A good reservoir characterization is deemed essential in the accomplishment of Enhanced Oil Recovery (EOR) processes in order to estimate accurately the properties of the porous medium affecting the flow properties. Several techniques at a field scale are currently being used to determine these properties, which are time and money consuming. But these alone do not guarantee the success of the project. Reservoir simulation and numerical techniques were then included in the pre-development and follow-up studies as an effective tool to determine the productivity and future behavior of the oil field. As the computational power increased, more advanced and detailed models were developed, including different chemical and physical phenomena. But alongside this process, there was an active research in the area of reservoir simulation, improving the accuracy and efficiency of the numerical schemes used for the flow, transport, and energy equations. The aim of this review is to address the topics described. Firstly, the origin of an oil recovery process, the economic factors and field tests involved are introduced. Secondly, the oil and porous medium origin and characterization as well as an introduction to the fundamental concepts and equations are associated to reservoir simulation. Finally, a brief description and analysis of the techniques are used in reservoir simulation employing finite difference methods, their downsides and possible ways to overcome these problems.

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Computation of the Longitudinal and Transverse Dispersion Coefficient in an Adsorbing Porous Medium Using Homogenization

Cited by 19 publications

References 64 publications

The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

The calculations of dispersion coefficients inside two‐dimensional randomly packed beds of circular particles

Gravity-Enhanced Transfer between Fracture and Matrix in Solvent-Based Enhanced Oil Recovery

Methods in Oil Recovery Processes and Reservoir Simulation

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