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

Abstract

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.

Electrical Conductivity of Partially Saturated Packings of Particles

“…The crossover saturation that we identify, S wx = 0.5, should be compared with 0.75, reported by Kiefer et al (2009), while Z = 8 is consistent with the range of connectivities reported by Makse et al (2000), Aste et al (2006), Bhattad et al (2011) andJiang et al (2016) for packings of spheres. As was the case for the drainage, the effect of the PSD and PCD on σ r is negligible.…”

Versatile and efficient pore network extraction method using marker-based watershed segmentation

“…Direct computation using only the pore and throat sizes, assuming cylindrical throats and spherical pores, gives K = 0.0685 × 10 −12 m 2 for the maximal ball network and K = 0.0801 × 10 −12 m 2 for the SNOW network, which is remarkably similar given the differences in the size and density of the pores and throats. This surprising behavior was noted by Bhattad et al [23] and attributed to the increase in the number of flow conduits offsetting the smaller pores. Both of these values are much too low: Dong and Blunt give the experimental value for Berea as K = 0.650 × 10 −12 , which is nearly 10× higher than the model prediction, and the permeability of the image was calculated using the lattice-Boltzmann method as K = 1.286 × 10 −12 , yet another factor of 2 higher.…”

“…This behavior is important since it will alter the size distribution of the network to make it more representative of the real material. Despite Bhattad et al [23] showing that this has surprisingly little impact on the transport properties of the network, researchers will expect physically representative size distributions for comparison to other measurements. The second benefit of this step is that several thin regions that were not caught by the saddle point check are removed.…”

“…This task is substantially more complicated than obtaining other metrics such as the chord length distribution or the two-point correlation function, which are straightforward applications of basic image analysis tools [22]. As made abundantly clear by Bhattad et al [23] in their excellent review article comparing network extraction techniques, the main challenge is the unclear definition of a pore and throat, and identifying where one pore ends and another begins, especially in three dimensions (3D). The currently favored method for extracting pore networks from images is based on the maximal ball algorithm proposed by Silin and Patzek [24], and refined by Blunt and co-workers [25,26].…”

Pore Structure Characterization of Indiana Limestone and Pink Dolomite from Pore Network Reconstructions

“…A connected line normal to the solid wall of the largest such radii defines the skeleton of the topological network, for both pores and throats [60][61][62][63][64].…”