Effects of fractal trajectory on gas diffusion in porous media

AIChE Journal ◽  
2003 ◽  
Vol 49 (12) ◽  
pp. 3037-3047 ◽  
Author(s):  
Baoquan Zhang ◽  
Xiufeng Liu
Keyword(s):  
Porous Media ◽  
Gas Diffusion ◽  
Diffusion In Porous Media

Gas Diffusion in Porous Media

Science ◽  
1959 ◽  
Vol 130 (3367) ◽  
pp. 100-102 ◽  
Author(s):  
R. J. MILLINGTON
Keyword(s):  
Porous Media ◽  
Gas Diffusion ◽  
Diffusion In Porous Media

Measuring the surface diffusivity of argon in nanoporous carbon

2017 ◽  
Vol 19 (8) ◽  
pp. 5855-5860 ◽  
Author(s):  
Xi Mi ◽  
Yunfeng Shi
Keyword(s):  
Porous Media ◽  
Trajectory Analysis ◽  
Bulk Diffusion ◽  
Gas Diffusion ◽  
Nanoporous Carbon ◽  
Surface Hopping ◽  
Surface Diffusivity ◽  
Diffusion In Porous Media ◽  
Transport Diffusivity

Gas diffusion in porous media consists of surface hopping and non-surface ballistic/bulk diffusion. Unfortunately, only the overall diffusivity is usually measured, without being separated into various diffusion modes. Here we used the “gravitation method” for measuring transport diffusivity, and utilized a detailed trajectory analysis to calculate the surface diffusivity and non-surface diffusivity.

Universal scaling of gas diffusion in porous media

2014 ◽  
Vol 50 (3) ◽  
pp. 2242-2256 ◽  
Author(s):  
Behzad Ghanbarian ◽  
Allen G. Hunt
Keyword(s):  
Porous Media ◽  
Gas Diffusion ◽  
Universal Scaling ◽  
Diffusion In Porous Media

On multicomponent gas diffusion and coupling concepts for porous media and free flow: a benchmark study

2021 ◽  
Author(s):  
Navid Ahmadi ◽  
Katharina Heck ◽  
Massimo Rolle ◽  
Rainer Helmig ◽  
Klaus Mosthaf
Keyword(s):  
Porous Media ◽  
Gas Diffusion ◽  
Free Flow ◽  
Benchmark Study ◽  
Multicomponent Gas

Modeling of natural-gas diffusion in oil-saturated tight porous media

Fuel ◽  
2021 ◽  
Vol 300 ◽  
pp. 120999
Author(s):  
Mohammad Hossein Doranehgard ◽  
Son Tran ◽  
Hassan Dehghanpour
Keyword(s):  
Porous Media ◽  
Natural Gas ◽  
Gas Diffusion ◽  
Tight Porous Media

scholarly journals Anomalous diffusion in porous media

2016 ◽  
Vol 40 (3) ◽  
pp. 1850-1862 ◽  
Author(s):  
J.A. Ferreira ◽  
G. Pena ◽  
G. Romanazzi
Keyword(s):  
Porous Media ◽  
Anomalous Diffusion ◽  
Diffusion In Porous Media

Determination of gas diffusion coefficients in saturated porous media: He and CH4 diffusion in Boom Clay

2013 ◽  
Vol 83-84 ◽  
pp. 217-223 ◽  
Author(s):  
Elke Jacops ◽  
Geert Volckaert ◽  
Norbert Maes ◽  
Eef Weetjens ◽  
Joan Govaerts
Keyword(s):  
Porous Media ◽  
Diffusion Coefficients ◽  
Gas Diffusion ◽  
Saturated Porous Media ◽  
Boom Clay

Electrical, Diffusional, and Hydraulic Tortuosity Anisotropy Quantification Using 3D CT-Scan Image Data

2021 ◽  
Author(s):  
Andres Gonzalez ◽  
Zoya Heidari ◽  
Olivier Lopez
Keyword(s):  
Porous Media ◽  
Fluid Flow ◽  
Ct Scan ◽  
Numerical Simulations ◽  
Potential Distribution ◽  
Image Data ◽  
Gas Diffusion ◽  
Depth Interval ◽  
Micro Ct ◽  
Multi Scale

Abstract Depositional mechanisms of sediments and post-depositional process often cause spatial variation and heterogeneity in rock fabric, which can impact the directional dependency of petrophysical, electrical, and mechanical properties. Quantification of the directional dependency of the aforementioned properties is fundamental for the appropriate characterization of hydrocarbon-bearing reservoirs. Anisotropy quantification can be accomplished through numerical simulations of physical phenomena such as fluid flow, gas diffusion, and electric current conduction in porous media using multi-scale image data. Typically, the outcome of these simulations is a transport property (e.g., permeability). However, it is also possible to quantify the tortuosity of the media used as simulation domain, which is a fundamental descriptor of the microstructure of the rock. The objectives of this paper are (a) to quantify tortuosity anisotropy of porous media using multi-scale image data (i.e., whole-core CT-scan and micro-CT-scan image stacks) through simulation of electrical potential distribution, diffusion, and fluid flow, and (b) to compare electrical, diffusional, and hydraulic tortuosity. First, we pre-process the images (i.e., CT-scan images) to remove non-rock material visual elements (e.g., core barrel). Then, we perform image analysis to identify different phases in the raw images. Then, we proceed with the numerical simulations of electric potential distribution. The simulation results are utilized as inputs for a streamline algorithm and subsequent direction-dependent electrical tortuosity estimation. Next, we conduct numerical simulation of diffusion using a random walk algorithm. The distance covered by each walker in each cartesian direction is used to compute the direction-dependent diffusional tortuosity. Finally, we conduct fluid-flow simulations to obtain the velocity distribution and compute the direction-dependent hydraulic tortuosity. The simulations are conducted in the most continuous phase of the segmented whole-core CT-scan image stacks and in the segmented pore-space of the micro-CT-scan image stacks. Finally, the direction-dependent tortuosity values obtained with each technique are employed to assess the anisotropy of the evaluated samples. We tested the introduced workflow on dual energy whole-core CT-scan images and on smaller scale micro-CT-scan images. The whole-core CT-scan images were obtained from a siliciclastic depth interval, composed mainly by spiculites. Micro-CT-scan images we obtained from Berea Sandstone and Austin Chalk formations. We observed numerical differences in the estimates of direction-dependent electrical, diffusional, and hydraulic tortuosity for both types of image data employed. The highest numerical differences were observed when comparing electrical and hydraulic tortuosity with diffusional tortuosity. The observed differences were significant specially in anisotropic samples. The documented comparison provides useful insight in the selection process of techniques for estimation of tortuosity. The use of core-scale image data in the proposed workflow provides semi-continuous estimates of tortuosity and tortuosity anisotropy which is typically not attainable when using pore-scale images. Additionally, the semi-continuous nature of the tortuosity and tortuosity anisotropy estimates in whole-core CT-scan image data provides an excellent tool for the selection of core plugs coring locations.

Numerical Analysis of Thermal-Solutal Convection in Heterogeneous Porous Media

2005 ◽  
Vol 73 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Charles-Guobing Jiang ◽  
M. Ziad Saghir ◽  
M. Kawaji
Keyword(s):  
Porous Media ◽  
Thermal Diffusion ◽  
Soret Effect ◽  
Heterogeneous Porous Media ◽  
Soret Coefficient ◽  
Solutal Convection ◽  
Coefficient Distribution ◽  
Separation Ratio ◽  
Diffusion In Porous Media ◽  
Vertical Cavity

Thermal diffusion, or Soret effect, in porous media is mathematically modeled with the Firoozabadi model based on non-equilibrium thermodynamics. The Soret effect in a binary mixture is investigated in a vertical cavity with heterogeneous permeability, where natural convection can occur. The thermo solutal convection with heterogeneous permeability was studied in terms of flow pattern, concentration distribution, component separation ratio, and Soret coefficient distribution. A consistent analysis was conducted and it is concluded that the Soret coefficient of thermal diffusion in porous media strongly depends on the heterogeneity of permeability.

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