Wicke–Kallenbach and Graham's diffusion cells: Limits of application for low surface area porous solids

Soukup, Karel; Schneider, P.; Šolcová, Olga

doi:10.1016/j.ces.2008.06.020

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…However, measurements with these cells require strictly isobaric conditions in cell compartments. Soukup et al [2008] indicated that even a small pressure difference (7–9 Pa) can lead to a significant diffusion flux deviation for samples with pores larger than 7 μm. This can be problematic for geological porous media having pores larger than 7 μm, while it is challenging to achieve a strict isobaric condition.…”

Section: Introductionmentioning

confidence: 99%

Diffusivity of rocks: Gas diffusion measurements and correlation to porosity and pore size distribution

Peng

Hamamoto

2012

Water Resources Research

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[1] Diffusivity was measured for 12 rock and construction material samples using a diffusion chamber method with oxygen as the tracer gas. Several steps were implemented to minimize leakage between the sample and the core holder, and rigorous tests were performed to evaluate and correct the overall leakage of the diffusion apparatus. This method was proven capable of rapidly measuring the diffusion coefficient for consolidated samples having dimensionless diffusivity values greater than 4.7 Â 10 À4 in a relative short duration (hours to 1 day). Gas diffusion measurements were also conducted for 11 repacked sediments and sands. Our results are consistent with literature data from liquid tracer through-diffusion methods; the diffusivity versus porosity relationship for our data can be described by Archie's law. The m value in Archie's law was found to be correlated to pore size: the finer the pore size is, the larger the m value is. A linear regression equation can describe the change of m with ln d 50 (the volumetric mean pore diameter) for most rocks with d 50 < 1.3 mm, while the outliers can be correlated to narrower pore size distribution.Citation: Peng, S., Q. Hu, and S. Hamamoto (2012), Diffusivity of rocks: Gas diffusion measurements and correlation to porosity and pore size distribution, Water Resour.

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Section: Introductionmentioning

confidence: 99%

Diffusivity of rocks: Gas diffusion measurements and correlation to porosity and pore size distribution

Peng

Hamamoto

2012

Water Resources Research

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“…Experimental studies obeyed this relationship for isobaric counterdiffusion (Remick and Geankoplis, 1973;Knaff and Schlünder, 1985). Conversely, pressure change was observed for isovolumetric counterdiffusion (Asaeda et al, 1981;Veldsink et al, 1994;Soukup et al, 2008). Although models have also been proposed to explain these results, they involve Knudsen diffusivity, which depends on molecular weight.…”

Section: Discussionmentioning

confidence: 85%

Mechanism of the Initial Phenomena of Defluidization Caused by Switching Fluidizing Gases

Kai

Hirano

Nakazato

et al. 2014

J. Chem. Eng. Japan

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In a uidized bed, particle agglomeration and channeling have been observed when the uidizing gas is switched from lower to higher density. This de uidization is a transient phenomenon, and uidization is restored after several minutes. In this paper, we assume this behavior can be explained by non-equimolar di usion, which causes pressure gradients in the bed and subsequently leads to viscous ow. To verify this assumption, pressure changes were measured for several binary di usion systems in a packed bed. After the packed bed was lled with a gas, another gas was supplied to the bed's outer surface to replace the rst gas. These gases were exchanged by di usion, and the pressure at the closed end of the column either increased or decreased due to di erences in the gases' molecular weights. The pressure changes were compared with pressure changes calculated from a model based on both an unsteady-state di usion equation and the Kozeny-Carman equation. The measured pressure changes in the packed beds could be correlated with the calculated values without any adjustable parameters. In a uidized bed, after the gas switched from lower to higher density, di erences in di usion rates due to the gases' various molecular weights caused a net molecular ow from the emulsion phase to the bubble phase. This ow caused a decrease in gas velocity in the emulsion phase and a contraction of the emulsion phase; channeling and de uidization were then observed.

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“…In conventional approaches, the solute concentration (and its spatial gradient) is measured directly by sampling. Examples include but are not limited to methods based on (i) Graham's diffusion tube [141], (ii) Diaphragm cell [5,142,143], (iii) Taylor dispersion/chromatography [6,8,18,[76][77][78][79][80]144] and (iv) the Wicke-Kallenbach cell [145][146][147]. In these approaches, while the direct measurement of composition of gas and/or liquid may provide a spatial gradient of concentration, they may be system intrusive (due to subsequent sampling), as well as time consuming (due to the diffusion being a slow process with diffusion time being large, as large as on the order of weeks to months depending on the system of interest and devices used in the measurements).…”

Section: Measurement Techniquesmentioning

confidence: 99%

The Role of Diffusivity in Oil and Gas Industries: Fundamentals, Measurement, and Correlative Techniques

Ratnakar

Dindoruk

2022

Processes

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The existence of various native or nonnative species/fluids, along with having more than one phase in the subsurface and within the integrated production and injection systems, generates unique challenges as the pressure, temperature, composition and time (P-T-z and t) domains exhibit multi-scale characteristics. In such systems, fluid/component mixing, whether for natural reasons or man-made reasons, is one of the most complex aspects of the behavior of the system, as inherent compositions are partially or all due to these phenomena. Any time a gradient is introduced, these systems try to converge thermodynamically to an equilibrium state while being in the disequilibrium state at scale during the transitional process. These disequilibrium states create diffusive gradients, which, in the absence of flow, control the mixing processes leading to equilibrium at a certain time scale, which could also be a function of various time and length scales associated with the system. Therefore, it is crucial to understand these aspects, especially when technologies that need or utilize these concepts are under development. For example, as the technology of gas-injection-based enhanced oil recovery, CO2 sequestration and flooding have been developed, deployed and applied to several reservoirs/aquifers worldwide, performing research on mass-transfer mechanisms between gas, oil and aqueous phases became more important, especially in terms of optimal design considerations. It is well-known that in absence of direct frontal contact and convective mixing, diffusive mixing is one of most dominant mass-transfer mechanisms, which has an impact on the effectiveness of the oil recovery and gas injection processes. Therefore, in this work, we review the fundamentals of diffusive mixing processes in general terms and summarize the theoretical, experimental and empirical studies to estimate the diffusion coefficients at high pressure—temperature conditions at various time and length scales relevant to reservoir-fluid systems.

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Wicke–Kallenbach and Graham's diffusion cells: Limits of application for low surface area porous solids

Cited by 11 publications

References 20 publications

Diffusivity of rocks: Gas diffusion measurements and correlation to porosity and pore size distribution

Diffusivity of rocks: Gas diffusion measurements and correlation to porosity and pore size distribution

Mechanism of the Initial Phenomena of Defluidization Caused by Switching Fluidizing Gases

The Role of Diffusivity in Oil and Gas Industries: Fundamentals, Measurement, and Correlative Techniques

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