Multiscale pore‐network representation of heterogeneous carbonate rocks

Abstract

A multiscale network integration approach introduced by Jiang et al. (2013) is used to generate a representative pore‐network for a carbonate rock with a pore size distribution across several orders of magnitude. We predict the macroscopic flow parameters of the rock utilising (i) 3‐D images captured by X‐ray computed microtomography and (ii) pore‐network flow simulations. To capture the multiscale pore size distribution of the rock, we imaged four different rock samples at different resolutions and integrated the data to produce a pore‐network model that combines information at several length‐scales that cannot be recovered from a single tomographic image. A workflow for selection of the number and length‐scale of the required input networks for the network integration process, as well as fine tuning the model parameters is presented. Mercury injection capillary‐pressure data were used to evaluate independently the multiscale networks. We explore single‐scale, two‐scale, and three‐scale network models and discuss their representativeness by comparing simulated capillary‐pressure versus saturation curves with laboratory measurements. We demonstrate that for carbonate rocks with wide pore size distributions, it may be required to integrate networks extracted from two or three discrete tomographic data sets in order to simulate macroscopic flow parameters.

Characterisation of pore structures of pharmaceutical tablets: A review

“…Specific challenges in image processing include the potentially large errors that are the result of performing the analysis on individual cross-sectional slices in 2D rather than 3D analysis, which creates directional bias and ignores geometrical information from planes directly above or below each 2D slice (Iassonov et al, 2009;Kaestner et al, 2008). Another image processing artefact is the partial volume effect, where features smaller than one voxel become upscaled to one voxel (Pak et al, 2016). An XµCTspecific image processing problem are real ring artefacts, due to detector drift, and quasi-ring artefacts, due to inconsistent X-ray beam intensity (Antoine et al, 2002).…”

“…Kaestner et al, 2008, note the importance of removing these before further processing. Additionally, if pore sizes span several orders of magnitude, images at different XµCT resolutions need to be combined to create accurate, multi-scale pore network models (Pak et al, 2016). The final stage is quantifying the image processing error -which is infrequently done in the literature.…”

Diagenetic influence on reservoir quality evolution, examples from Triassic conglomerates/arenites in the Edvard Grieg field, Norwegian North Sea

“…Knackstedt et al, 2012;Bin et al, 2013;Bultreys et al, 2015) and carbonates (e.g. Lopez et al, 2012;Prodanovic et al, 2015;Pak et al, 2016;Fheed et al, 2018). Authigenic clays and carbonate cements have a strong influence on porosities and pore-throat geometries and hence reservoir quality by profoundly modifying the nano-to macroscale porosity and permeability characteristics .…”

Using Resin‐Based 3D Printing to Build Geometrically Accurate Proxies of Porous Sedimentary Rocks

“…Numerical simulation (e.g., Blunt ; Pak et al ) offers a faster and nondestructive way to build pore network models and to calculate transport properties. These computational methods are typically based on analog (e.g., CAD) or image‐derived (e.g., computed tomography, CT) pore‐network topologies that allow us to simulate coupling between physical and chemical processes to predict transport properties (Ryazanov et al ; Van der Land et al ).…”