The effect of X‐ray micro computed tomography image resolution on flow properties of porous rocks

Latief, Fourier Dzar Eljabbar; Fauzi, Umar; Irayani, Zaroh; Dougherty, Geoff

doi:10.1111/jmi.12521

Cited by 26 publications

(8 citation statements)

References 37 publications

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“…This is problematic because porosity and fluid flow are 3D properties, thus they should be simulated in 3D.This research builds on the efforts of Wang et al [82,83,85] and Shams et al[88] to super-resolve, or improve the resolution, of lower resolution scans and it will more specifically be applying 3D generative adversarial neural networks. In order to have the most solid neural network, two CT scans taken at different resolution of exactly the same volume are used for training, because artificial downsampling of a high-resolution image to get a low resolution image is not performed without the risk of oversimplifying the problem [32,89]. The effect of oversimplification is further elaborated on in this research by studying the effect of mechanically versus artificially reduced resolution on the pore network and related fluid flow.…”

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution versus sample size trade-off. Therefore, a super-resolution method using generative adversarial neural networks (GANs) was used to artificially improve the resolution. Firstly, the influence of resolution on pore network properties and single-phase, unsaturated, and two-phase flow was analysed to verify that pores and pore throats become larger on average and surface area decreases with worsening resolution. These observations are reflected in increasingly overestimated single-phase permeability, less moisture uptake at lower capillary pressures, and high residual oil fraction after waterflooding. Therefore, the super-resolution GANs were developed which take low (12 µm) resolution input and increase the resolution to 4 µm, which is compared to the expected high-resolution output. These results better predicted pore network properties and fluid flow properties despite the overestimation of porosity. Relevant small pores and pore surfaces are better resolved thus providing better estimates of unsaturated and two-phase flow which can be heavily influenced by flow along pore boundaries and through smaller pores. This study presents the second case in which GANs were applied to a super-resolution problem on geological materials, but it is the first one to apply it directly on raw CT images and to determine the actual impact of a super-resolution method on fluid predictions.Materials 2020, 13, 1397 2 of 33 examples requires detailed knowledge on fluid storage and flow properties which could be estimated through experiments, either carried out in specialized laboratories or deduced from digital rocks. Laboratory measurements, however, require expensive and complex set-ups, take a long time to complete, are often destructive, do not always entirely capture the studied properties and on top of that, experiments often fail to produce reproducible results between laboratories [9,[12][13][14][15][16][17][18][19]. Therefore, numerical solutions have been developed allowing to compute fluid storage and flow properties on digital rocks [7,[20][21][22][23]. Such digital rocks are acquired through direct imaging or numerical simulations of rocks.Computed tomography (CT) is a technique to rapidly capture the three-dimensional structure of materials [22,24,25]. However, like any imaging technique, CT is plagued by a sample size versus resolution trade-off, which is adversely affecting fluid flow simulations. Both a high resolution and sufficiently large volume are required for accurate fluid flow estimations at the pore-scale [9,[26][27][28]. Resolution impacts how well pore network topology and pore surfaces are resolved. At lower resolution, i.e., lower resolving power, pore sizes are often overestimated and specific surf...

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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“…Although these variations in image resolution are known to directly affect measurements of crack properties, such as surface area (increased surface area is generally detected with decreasing voxel sizes), some estimation of the magnitude of this effect can already be accounted for based on a recent study [ 46 ]. The results of this study indicate that even a doubling of voxel size does not appear to generally change the measured crack surface area by more than a factor around two.…”

Section: Methodsmentioning

confidence: 99%

Detection and Quantification of Cracking in Concrete Aggregate through Virtual Data Fusion of X-Ray Computed Tomography Images

2020

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In this work, which is part of a larger research program, a framework called “virtual data fusion” was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray computed tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in alkali-silica reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then “fused” in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.

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“…Recently, several groups have looked into many aspects of the permeability prediction problem. For instance, an attempt has been made at quantifying the permeability anisotropy in carbonates [18], an inverse approach has been adopted in which the measured porosity is used as a starting point to estimate the permeability [19], multiscale microtomography has been used to try and obtain a reasonable representation of the pore-network structure [20][21][22], further attempts have been made at quantifying the effect of image resolution on simulated permeability [23,24]. Because of the opaque nature of most porous media, permeability is usually obtained by measuring flow speed under a certain pressure gradient in experiments.…”

Section: Introductionmentioning

confidence: 99%

Consistent prediction of absolute permeability in carbonates without upscaling

Khodja

Hussaini

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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We describe a study focused on the absolute permeability of reservoir carbonate rocks from the Middle East and involving comparison of experimental data and numerical estimates obtained by combining digital-rock and Lattice-Boltzmann Methods (LBM). The question of the “representativeness” of the site at which the simulation is performed is addressed as follows. First, a low-resolution, CT X-ray scan of the core plug is performed to identify regions of large porosity (millimeter-sized vugs, etc.). These regions are then avoided to postselect smaller sites (site volume ~ 1 mm3) which are to be scanned at higher resolutions (voxel size < dominant pore-throat size of the core plug). A “representativeness” criterion based on an empirically-inspired “representativeness” measure (R-measure) is used to eliminate those sites for which R > b, where b is an upper bound (typically, b = 1). Essentially, the measure estimates how well the postselected sites capture the experimental porosity and the dominant pore-throat size of the core plug. This leads to a small set of sites for which the simulations are both computationally manageable and yield a reasonable estimate of the permeability: the experimental and predicted values differ by a factor of about 3 on average, which is a particularly significant result given the challenging heterogeneous pore space of carbonate samples. We believe the suggested methodology to be an adequate and practical way to circumvent upscaling.

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The effect of X‐ray micro computed tomography image resolution on flow properties of porous rocks

Cited by 26 publications

References 37 publications

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Detection and Quantification of Cracking in Concrete Aggregate through Virtual Data Fusion of X-Ray Computed Tomography Images

Consistent prediction of absolute permeability in carbonates without upscaling

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