Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media
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- 作者：Pradeep Bhattad (1) pradeep@lsu.edu
Clinton S. Willson (2) cwillson@lsu.edu
Karsten E. Thompson (1) karsten@lsu.edu - 关键词：Pore network generation – ; Pore network modeling – ; Permeability – ; Pore structure characterization – ; 3D image processing – ; X ; ray micro ; tomography
- 刊名：Transport in Porous Media
- 出版时间：November 2011
- 出版年：2011
- 期刊代码：69_01693913
- 类别：ch
- 卷：90
- 期：2
- 页码：363-391
- 数据来源：sp
摘要
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.