[1] Fluid flow through porous media, and the thermal, electrical, and acoustic properties of these materials, is largely controlled by the geometry and topology (GT) of the pore system, which can be considered as a network. Network extraction techniques have been applied in many research fields, including shape representation, pattern recognition, and artificial intelligence. However, the set of algorithms presented here significantly improves the efficiency of common thinning algorithms by introducing a sufficiency condition based on the idea of a simple set. This paper describes an efficient and accurate algorithm for extracting the geometrical/topological network that represents the pore structure of a porous medium, referred to as the GT network. The accurate medial axis and the specific GT description of the network are achieved by applying symmetrical and interval strategies during the erosion step in the image processing. The GT network extraction algorithm presented here involves a number of steps, including (1) calculation of the three-dimensional Euclidean distance map; (2) clustering of voxels; (3) extraction of the network of the pore space; (4) partitioning of the pore space; and (5) computation of shape factors. The focus of this paper is mainly on the thinning method that underpins points 1-3. The paper is primarily a method description, but we illustrate the functionality of the technique by extracting a pore scale GT network from microcomputer tomography images of three sandstones.

The creation of a 3D pore-scale model of a porous medium is often an essential step in quantitatively characterising the medium and predicting its transport properties. Here we describe a new stochastic pore space reconstruction approach that uses thin section images as its main input. The approach involves using a third-order Markov mesh where we introduce a new algorithm that creates the reconstruction in a single scan, thus overcoming the computational issues normally associated with Markov chain methods. The technique is capable of generating realistic pore architecture models (PAMs), and examples are presented for a range of fairly homogenous rock samples as well as for one heterogeneous soil sample. We then apply a Lattice-Boltzmann (LB) scheme to calculate the permeabilities of the PAMs, which in all cases closely match the measured values of the original samples. We also develop a set of software methods -referred to as pore analysis tools (PATs) -to quantitatively analyse the reconstructed pore systems. These tools reveal the pore connectivity and pore size distribution, from which we can simulate the mercury injection process, which in turn reproduces the measured curves very closely. Analysis of the topological descriptors reveals that a connectivity function based on the specific Euler number may serve as a simple predictor of the threshold pressure for geo-materials.

[1] Developing a better understanding of single-/multiphase flow through reservoir rocks largely relies on characterizing and modeling the pore system. For simple homogeneous rock materials, a complete description of the real pore structure can be obtained from the pore network extracted from a rock image at a single resolution, and then an accurate prediction of fluid flow properties can be achieved by using network model. However, for complex rocks (e.g., carbonates, heterogeneous sandstones, deformed rocks), a comprehensive description of the real pore structure may involve several decades of length scales (e.g., from submicron to centimeters), which cannot be captured by a singleresolution image due to the restriction of image size and resolution. Hence, the reconstruction of a single 3-D multiple-scale model of a porous medium is an important step in quantitatively characterizing such heterogeneous rocks and predicting their multiphase flow properties. In this paper, we present a novel methodology for the numerical construction of the multiscale pore structure of a complex rock from a number of CT images/models of a carbonate sample at several length scales. The success of this reconstruction relies heavily on image segmentation, pore network extraction and stochastic network generation, which are provided by our existing software system, referred to as Pore Analysis Tools (PAT). Specifically, the statistical description of pore networks of 3-D rock images at multiple resolutions makes it possible for us to: (a) construct an arbitrary sized network which is equivalent in a specified domain, and (b) integrate multiple networks of different sizes into a single network incorporating all scales. Using multiscale networks of carbonate rocks generated in this manner, two-phase network modeling results are presented to show how the resulting flow properties are dependent on inclusion of information from multiple scales. These outcomes reinforce the importance of capturing both geometry and topology in the hierarchical pore structure for such complex pore systems. The example presented reveals that isolated large-scale (e.g., macro-) pores are mainly connected by small-scale (e.g., micro-) pores, which in turn determines the combined effective petrophysical properties (capillary pressure, absolute and relative permeability). It is also demonstrated that multi-(three) scale networks reveal the effects of the interacting multiscale pore systems (e.g., micropores, macropores, and vugs) on bulk flow properties in terms of two-phase flow properties.

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Pore networks can be extracted from 3D rock images to accurately predict multiphase flow properties of rocks by network flow simulation. However, the predicted flow properties may be sensitive to the extracted pore network if it is small, even though its underlying characteristics are representative. Therefore, it is a challenge to investigate the effects on flow properties of microscopic rock features individually and collectively based on small samples. In this article, a new approach is introduced to generate from an initial network a stochastic network of arbitrary size that has the same flow properties as the parent network. Firstly, we characterise the realistic parent network in terms of distributions of the geometrical pore properties and correlations between these properties, as well as the connectivity function describing the detailed network topology. Secondly, to create a stochastic network of arbitrary size, we generate the required number of nodes and bonds with the correlated properties of the original network. The nodes are randomly located in the given network domain and connected by bonds according to the strongest correlation between node and bond properties, while honouring the connectivity function. Thirdly, using a state-of-the-art two-phase flow network model, we demonstrate for two samples that the rock flow properties (capillary pressure, absolute and relative permeability) are preserved in the stochastic networks, in particular, if the latter are larger than the original, or the method reveals that the size of the original sample is not representative. We also show the information that is necessary to reproduce the realistic networks correctly, in particular the connectivity function. This approach forms the basis for the stochastic generation of networks from multiple rock images at different resolutions by combining the relevant statistics from the corresponding networks, which will be presented in a future publication.

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Abstract:Modern scanning electron microscopes often include software that allows for the possibility of obtaining large format high-resolution image montages over areas of several square centimeters. Such montages are typically automatically acquired and stitched, comprising many thousand individual tiled images. Images, collected over a regular grid pattern, are a rich source of information on factors such as variability in porosity and distribution of mineral phases, but can be hard to visually interpret. Additional quantitative data can be accessed through the application of image analysis. We use backscattered electron (BSE) images, collected from polished thin sections of two limestone samples from the Cretaceous of Brazil, a Carboniferous limestone from Scotland, and a carbonate cemented sandstone from Northern Ireland, with up to 25,000 tiles per image, collecting numerical quantitative data on the distribution of porosity. Images were automatically collected using the FEI software Maps, batch processed by image analysis (through ImageJ), with results plotted on 2D contour plots with MATLAB. These plots numerically and visually clearly express the collected porosity data in an easily accessible form, and have application for the display of other data such as pore size, shape, grain size/shape, orientation and mineral distribution, as well as being of relevance to sandstone, mudrock and other porous media.