Universal scaling of gas diffusion in porous media

2014 ◽  
Vol 50 (3) ◽  
pp. 2242-2256 ◽  
Author(s):  
Behzad Ghanbarian ◽  
Allen G. Hunt
Science ◽  
1959 ◽  
Vol 130 (3367) ◽  
pp. 100-102 ◽  
Author(s):  
R. J. MILLINGTON

2017 ◽  
Vol 19 (8) ◽  
pp. 5855-5860 ◽  
Author(s):  
Xi Mi ◽  
Yunfeng Shi

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.


AIChE Journal ◽  
2003 ◽  
Vol 49 (12) ◽  
pp. 3037-3047 ◽  
Author(s):  
Baoquan Zhang ◽  
Xiufeng Liu

2014 ◽  
Vol 13 (4) ◽  
pp. vzj2013.12.0204 ◽  
Author(s):  
Allen G. Hunt ◽  
Behzad Ghanbarian ◽  
Robert P. Ewing

Fractals ◽  
2015 ◽  
Vol 23 (01) ◽  
pp. 1540004 ◽  
Author(s):  
BEHZAD GHANBARIAN ◽  
ALLEN G. HUNT ◽  
THOMAS E. SKINNER ◽  
ROBERT P. EWING

Accurate prediction of the saturation dependence of different modes of transport in porous media, such as those due to conductivity, air permeability, and diffusion, is of broad interest in engineering and natural resources management. Most current predictions use a "bundle of capillary tubes" concept, which, despite its widespread use, is a severely distorted idealization of natural porous media. In contrast, percolation theory provides a reliable and powerful means to model interconnectivity of disordered networks and porous materials. In this study, we invoke scaling concepts from percolation theory and effective medium theory to predict the saturation dependence of modes of transport — hydraulic and electrical conductivity, air permeability, and gas diffusion — in two disturbed soils. Universal scaling from percolation theory predicts the saturation dependence of air permeability and gas diffusion accurately, even when the percolation threshold for airflow is estimated from the porosity. We also find that the non-universal scaling obtained from the critical path analysis (CPA) of percolation theory can make excellent predictions of hydraulic and electrical conductivity under partially saturated conditions.


Author(s):  
Navid Ahmadi ◽  
Katharina Heck ◽  
Massimo Rolle ◽  
Rainer Helmig ◽  
Klaus Mosthaf

Fuel ◽  
2021 ◽  
Vol 300 ◽  
pp. 120999
Author(s):  
Mohammad Hossein Doranehgard ◽  
Son Tran ◽  
Hassan Dehghanpour

2016 ◽  
Vol 40 (3) ◽  
pp. 1850-1862 ◽  
Author(s):  
J.A. Ferreira ◽  
G. Pena ◽  
G. Romanazzi

2013 ◽  
Vol 83-84 ◽  
pp. 217-223 ◽  
Author(s):  
Elke Jacops ◽  
Geert Volckaert ◽  
Norbert Maes ◽  
Eef Weetjens ◽  
Joan Govaerts

2021 ◽  
Author(s):  
Andres Gonzalez ◽  
Zoya Heidari ◽  
Olivier Lopez

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.


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