Morphological decomposition of sandstone pore–space: fractal power-laws

In this work we have investigated numerically the formation of channelised dissolution patterns, termed &#8220;wormholes&#8221;, using initial pore geometries generated from tomographic images of limestone cores. We have employed an OpenFOAM-based Darcy-scale numerical solver,&#160; porousFoam, which combines a Darcy/Darcy-Brinkman flow solver and a reactive transport solver in an evolving pore space. Simulated geometries, of both final and intermediate steps, are compared to dissolution experiments on samples the initial pore geometry is generated from, with the same acid concentration and flow rate applied.&#160;The initial condition of porosity distribution is set from X-Ray Computed Microtomography (XCMT) images via three phase segmentation into macroporosity, microporosity, and grain regions. Porosity values for microporous regions are set using linear interpolation between pore and grain grayscale values [1]. The inlet boundary conditions of flow rate and acid concentration are set as in the dissolution experiment. To test the effect of the permeability-porosity constitutive relationship we have investigated several options including power laws of varying exponent, and the Carman-Kozeny relation. We have also analyzed the impact of using Darcy versus Darcy-Brinkman flow solvers. Despite a qualitatively similar appearance to experimental results, the simulated wormholes are usually significantly thicker than their experimental counterparts, a fact noted by other researchers as well [2]. We comment on possible reasons for this discrepancy and on the limitations of Darcy-scale solvers in general. Additionally, we find that higher exponents in the power law makes the numerical dissolution very sensitive to grayscale threshold values as a small variation in this value changes the path of the wormhole.&#160;[1] Luquot, L., Rodriguez, O., and Gouze, P.: Experimental characterization of porosity structure and transport property changes in limestone undergoing different dissolution regimes, Transport Porous Med., 101, 507&#8211;532, 2014.[2] Yue Hao, Megan Smith, Yelena Sholokhova, Susan Carroll, CO2-induced dissolution of low permeability carbonates. Part II: Numerical modeling of experiments, Advances in Water Resources, 62, 388-408, 2013&#160;

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Correlation of Pore Structure and Permeability by SEM-Image Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100180896 ◽

1990 ◽

Vol 48 (1) ◽

pp. 428-429

Author(s):

C. A. Callender ◽

Wm. C. Dawson ◽

J. J. Funk

Keyword(s):

Image Analysis ◽

Pore Structure ◽

Imaging System ◽

Pore Space ◽

Simulation Models ◽

Image Analyzer ◽

Imaging Data ◽

Sem Images ◽

Thin Sections ◽

Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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