Transport properties of porous media from the microgeometry of a three-dimensional voronoi network

Vrettos, Nikolas A.; Imakoma, Hironobu; Okazaki, Morio

doi:10.1016/0255-2701(89)80022-4

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…For the glass spheres and the sand, an argument can be made that the GB1 networks are physically reasonable because the pore locations are derived from a Delaunay tessellation of the grain locations, which is an accepted method for discretizing the pore space in granular materials (Mellor 1989;Vrettos et al 1989;Bryant et al 1993;Thompson and Fogler 1997). These networks contain 4.96 and 6.50 pores/particle for the sphere pack and sand pack, respectively.…”

Section: Pore Densitymentioning

confidence: 99%

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

2011

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Image-based network modeling has become a powerful tool for modeling transport in real materials that have been imaged using X-ray computed micro-tomography (XCT) or other three-dimensional imaging techniques. Network generation is an essential part of image-based network modeling, but little quantitative work has been done to understand the influence of different network structures on modeling. We use XCT images of three different porous materials (disordered packings of spheres, sand, and cylinders) to create a series of four networks for each material. Despite originating from the same data, the networks can be made to vary over two orders of magnitude in pore density, which in turn affects network properties such as pore-size distribution and pore connectivity. Despite the orders-of-magnitude difference in pore density, single-phase permeability predictions remain remarkably consistent for a given material, even for the simplest throat conductance formulas. Detailed explanations for this beneficial attribute are given in the article; in general, it is a consequence of using physically representative network models. The capillary pressure curve generated from quasi-static drainage is more sensitive to network structure than permeability. However, using the capillary pressure curve to extract pore-size distributions gives reasonably consistent results even though the networks vary significantly. These results provide encouraging evidence that robust network modeling algorithms are not overly sensitive to the specific structure of the underlying physically representative network, which is important given the variety image-based network-generation strategies that have been developed in recent years. 123 364 P. Bhattad et al.

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Section: Pore Densitymentioning

confidence: 99%

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

2011

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“…Lattice-based structural models are amenable to analytical treatments of tortuosities, void isolation, and transport properties (Hollewand & Gladden, 1992;Reyes & Jensen, 1985;Sharratt & Mann, 1987;Vrettos, Imakoma, & Okazaki, 1989). One useful construct is a Bethe network consisting of a non-reconnecting inÿnite branching tree described fully by the number of branches emanating from each node (the coordination number Z).…”

Section: Implications For Description and Understanding Of Pore Strucmentioning

confidence: 99%

Monte-Carlo simulations of surface and gas phase diffusion in complex porous structures

Zalc

Reyes

Iglesia

2003

Chemical Engineering Science

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A new procedure for estimating surface di usivities and tortuosities within realistic models of complex porous structures is reported. Our approach uses Monte-Carlo tracer methods to monitor mean-square displacements for molecules restricted to wander on pore walls within model random mesoporous solids typical of those used as adsorbents, heterogeneous catalysts, and porous membranes. We consider model porous solids formed from initial packings of spheres with unimodal, Gaussian, or bimodal distributions of size; changes in pellet porosity are achieved by increasing microsphere radii and by randomly removing spheres from highly densiÿed packings in order to simulate densiÿcation and coarsening, respectively. Geometric tortuosities for the surface phase reached large values at void fractions near 0.04 and 0.42 for densiÿed solids; the surface tortuosity gave a minimum value of 1.9 at a void fraction of ∼0.26. These high tortuosities correspond to percolation thresholds for the void and solid phases, which in turn re ect packing densities at which each phase becomes discontinuous. Surface tortuosities for coarsened solids at low void fractions were similar to those in densiÿed solids; however, at void fractions above ∼0.3, surface tortuosities of coarsened solids increased only gradually with void fraction, because coarsening retains signiÿcant overlap among spheres at void fractions above those giving disconnected solids in densiÿed structures. Simulations of bulk di usion within voids were used to compare the transport properties and connectivity of the void space with those of surfaces that deÿne this void space. Surface and void tortuosities were similar, except for void fractions near the solid percolation threshold, because unconnected solid particles interrupt surface connectivity but not gas phase di usion paths. Surface and void tortuosities were also similar for channels within linear chains of overlapping hollow spheres as both tortuosities increased with decreasing extent of sphere overlap. These simulations provide a basis for estimates of surface and void tortuosities, which are essential in the interpretation and extrapolation of di usion rates in complex porous media. Surface and void di usivity estimates di ered signiÿcantly from those obtained from lattice and capillary models of complex porous structures. ?

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“…This model, however, contradicts the practical situation that pores are interconnected (Jin et al, 2015;). The network model adopts the connected capillary/fracture to describe the complex pore space of a natural reservoir and thus efficiently characterizes the connectivity and random distribution of pores (Ioannidis & Chatzis, 1993;Lindquist et al, 1996;Reeves & Celia, 1996;Vrettos et al, 1989). But few quantitative investigations by this model are focused on the dual-porosity structure in a coal matrix.…”

Section: Research Articlementioning

confidence: 99%

Characterizing the Complexity Assembly of Pore Structure in a Coal Matrix: Principle, Methodology, and Modeling Application

et al. 2020

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The pore structure in a coal matrix is a dual-porosity system where fractures and pores coexist and feature scale-invariance properties, which would affect the occurrence and migration of coalbed methane (CBM) significantly. Therefore, it is of fundamental importance to well define complexity types and effectively characterize their assembly mechanism of pore structure in a coal matrix. Here we identify the pore structure in a coal matrix as a dual-complexity system consisting of original complexity and behavioral complexity independent to each other, where the former determines the scaling types of single-or dual-porosity structure, while the latter dominates the scale-invariance properties of self-similarity, self-affinity, and multifractality. Next we clarify the essentials of scale-invariance properties and unify the definition of behavioral complexity. By employing Voronoi diagrams, we develop a dual-porosity coupling algorithm to describe the original complexity and set up a mathematical framework to characterize the complexity assembly in fractal dual-porosity media. For modeling demonstration, we select some typical coal samples from different reservoirs in China, extract the scale-invariance parameters, and establish fractal topography based on mercury intrusion porosimetry (MIP) and N 2 adsorption data. Using experimental tests, mathematical derivation, and numerical simulations in combination, we reveal the principle and methodology for the characterization of the complexity assembly in a coal matrix.

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Transport properties of porous media from the microgeometry of a three-dimensional voronoi network

Cited by 12 publications

References 16 publications

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

Monte-Carlo simulations of surface and gas phase diffusion in complex porous structures

Characterizing the Complexity Assembly of Pore Structure in a Coal Matrix: Principle, Methodology, and Modeling Application

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