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.
We report spontaneous supra-assembly of fibrous surfactant crystallites at the air-solution interface resulting in spectacular arrays of two-dimensional spiral and three-dimensional "micro-pottery"-like superstructures. Surface pressure differential driven bending of the embryonic fiber nuclei and Marangoni convection driven fiber migration/alignment appear to be the causal factors behind this phenomenon. The assemblies form at specific crystal-growth velocities dictated by the relative time scales for fiber bending/alignment and their rigidification/immobilization as they grow. Although our studies are restricted to a specific class of amphiphiles, namely, alkaline metal salts of linear fatty acids, the phenomenon should be generic to amphiphilic molecules that crystallize into flexible fibers.
Tight unconventional reservoirs have become an increasingly common target for hydrocarbon production. Exploitation of these resources requires a comprehensive reservoir description and characterization program to estimate reserves, identify properties which control production and account for fracturability. Multiscale imaging studies from whole core to the nanometer scale can aid in understanding the multiple contributions of heterogeneity, fracture density, pore types, pore connectivity, mineralogy and organic content to the petrophysical response and production characteristics. In this paper we describe examples of the application of a multiscale imaging and analysis method to characterize challenging unconventional reservoirs which incorporates: Geological rock typing and heterogeneity characterization at the core/plug scale (3D imaging and conventional descriptions); Mineralogy, primary grain structure and porosity/microporosity characterization at the pore scale via a range of 3D imaging technology (CT, micro-CT); SEM/SEM-EDS/FIBSEM analysis to reveal the nanoporous structure of important pore types (e.g, secondary porosity, microporous matrix, diagenetic minerals including clays); SEM and micro-CT analysis of wettability (applicable for oil reservoirs); Integration of image data to generate 3D model structures that honour the primary grain structure and accurately capture the nanoporous regions.The generation of integrated image-based microstructures provides the basis for the computation of key petrophysical and multiphase flow properties which impact on the storage capacity and production characteristics of the samples. Petrophysical properties are first calculated on various pore types at representative scales; these predictions are then upscaled to estimate the contributions to permeability, formation factor and elastic response of the key constituent phases (e.g., porosity and permeability associated with clays, slot-like pores, cement, and partially dissolved minerals (e.g. feldspars)) at the plug scale. Estimation of drainage relative permeability and capillary pressure from 3D image data and modelling are compared and predictions of flow properties derived. These predictions are compared/calibrated to high quality experimental data on the same or sister core material.
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