References and benchmarks for pore-scale flow simulated using micro-CT images of porous media and digital rocks

Saxena, Nishank; Hofmann, Ronny; Alpak, Faruk O.; Berg, Steffen; Dietderich, Jesse; Agarwal, Umang; Tandon, Kunj; Hunter, Sander; Freeman, Justin; Wilson, Ove Bjørn

doi:10.1016/j.advwatres.2017.09.007

Cited by 118 publications

(67 citation statements)

References 72 publications

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“…For the first two cases, the images representing the cross-sections were created using Paint.net, a graphics editor program, and then combined into a single 3-D image stack using ImageJ. The 3-D image stack for the pack of spheres was obtained from supplementary material provided by [31] and which is also available at [10]. The The main output of the LBM simulator is the local velocity field, u(x), which is the steady-state solution of the Navier-Stokes equations as recovered by the collision models.…”

Section: Numerical Testsmentioning

confidence: 99%

“…17. For reference values, we use a value of k sp ref = 280, 151mD which is the median value of permeability that was obtained using different LBM and non-LBM based fluid solvers on the same sphere pack image datasets as used in this study [31]. The an optimal range for all resolutions and geometries.…”

Section: Sphere Packmentioning

confidence: 99%

“…For direct numerical simulation of flow in microtomographic pores-spaces, the lattice Boltzmann method (LBM) has emerged as a method of choice [9,11,18,24,25,31,37]. After imaging, a cubic voxel in the 3-D image is segmented such that each voxel is labeled as either a fluid or a solid.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, also commonly called Digital Rock Physics (DRP) in the oil and gas industry, transport properties of rocks, such as absolute (intrinsic) permeability, relative permeability, dispersivity, formation factor, etc., are computed on geometries constructed from 3-D microtomographic images [2]. In this study, we focus on the permeability of a porous medium for single phase flow for Newtonian fluids.For direct numerical simulation of flow in microtomographic pores-spaces, the lattice Boltzmann method (LBM) has emerged as a method of choice [9,11,18,24,25,31,37]. After imaging, a cubic voxel in the 3-D image is segmented such that each voxel is labeled as either a fluid or a solid.…”

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confidence: 99%

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Lattice Boltzmann models for micro-tomographic pore-spaces

Rao

Schaefer

2019

Computers & Fluids

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The lattice Boltzmann method (LBM) is a popular numerical framework to investigate single and multiphase flow though porous media. For estimation of absolute permeability based on micro-tomographic images of the porous medium, the single-relaxation time (SRT) collision model is the most widely-used, although the multiple-relaxation-time (MRT) collision model also has recently acquired wider usage, especially for industrial applications. However, the SRT collision model and a sub-optimal choice of the MRT collision parameters can both lead to permeability predictions that depend on the relaxation time, τ . This parametric dependence is nonphysical for Stokes flow in porous media and also leads to much larger number of iterations required for convergence. In this paper, we performed a systematic numerical evaluation of the different sets of relaxation parameters in the D3Q19-MRT model for modeling Stokes flow in 3-D microtomographic pore-spaces using the bounceback scheme. These sets of parameters are evaluated from the point of view of accuracy, convergence rate, and an ability to generate parameter-independent permeability solutions. Instead of tuning all six independent relaxation rates that are available in the MRT model, the sets that were analyzed have relaxation rates that depend on one or two independent parameters, namely τ and Λ. We tested elementary porous media at different image resolutions and a random packing of spheres at relatively high resolution. We observe that sets of certain specific relaxation parameters (Sets B, D, or E as listed in Table 2), and τ in the range τ ∈ [1.0, 1.3] can result in best overall accuracy, convergence rate, and parameter-independent permeability predictions. IntroductionFlow and transport in reservoir rocks and other porous media are strongly dependent on the properties of the pore-space, such as the geometry, distribution, and connectivity. Laboratory-based core analysis are the main tools for studying and quantifying rock properties for practical and scientific applications.Over the past decade, high-resolution, three-dimensional (3-D), X-ray microtomography imaging and processing capabilities have become widely available [34]. These advances have enabled one to obtain a 3-D reconstructed pore-space domain which serve as an input for the direct numerical simulation of the underlying fluid and transport processes. In this approach, also commonly called Digital Rock Physics (DRP) in the oil and gas industry, transport properties of rocks, such as absolute (intrinsic) permeability, relative permeability, dispersivity, formation factor, etc., are computed on geometries constructed from 3-D microtomographic images [2]. In this study, we focus on the permeability of a porous medium for single phase flow for Newtonian fluids.For direct numerical simulation of flow in microtomographic pores-spaces, the lattice Boltzmann method (LBM) has emerged as a method of choice [9,11,18,24,25,31,37]. After imaging, a cubic voxel in the 3-D image is segmented such that each voxel is labe...

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Section: Numerical Testsmentioning

confidence: 99%

Section: Sphere Packmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Lattice Boltzmann models for micro-tomographic pore-spaces

Rao

Schaefer

2019

Computers & Fluids

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“…The particular motivation for this work is to acquire high spatial resolution MRI images of rock core plugs and the fluids within them, to aid the development and validation of flow simulators to predict multiphase flow in rocks. This field is now commonly referred to as Digital Rock physics or technology and is emerging as a powerful tool for understanding enhanced hydrocarbon recovery processes (Blunt et al ., ; Andrä et al ., , b; Saxena et al ., , b; Saxena et al ., ). To date, µCT has been used almost exclusively in Digital Rock physics as an essential tool for obtaining µm‐scale images of rocks (Blunt et al ., ; Berg et al ., ; Andrew et al ., ), wherein the image contrast is generated by the material phase density, which generates contrast between the pore space and solid matrix.…”

Section: Introductionmentioning

confidence: 97%

Identification of sampling patterns for high‐resolution compressed sensing MRI of porous materials: ‘learning’ from X‐ray microcomputed tomography data

Karlsons

Kort

Sederman

et al. 2019

Journal of Microscopy

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Summary There exists a strong motivation to increase the spatial resolution of magnetic resonance imaging (MRI) acquisitions so that MRI can be used as a microscopy technique in the study of porous materials. This work introduces a method for identifying novel data sampling patterns to achieve undersampling schemes for compressed sensing MRI (CS‐MRI) acquisitions, enabling 3D spatial resolutions of 17.6 µm to be achieved. A data‐driven learning approach is used to derive k‐space undersampling schemes for 3D MRI acquisitions from 3D X‐ray microcomputed tomography (µCT) datasets acquired at a higher spatial resolution than can be acquired using MRI. The performance of the new sampling approach was compared to other, well‐established sampling strategies using simulated MRI data obtained from high‐resolution µCT images of rock core plugs. These simulations were performed for a range of different k‐space sampling fractions (0.125–0.375) using images of Ketton limestone. The method was then extended to consideration of imaging Estaillades limestone and Fontainebleau sandstone. The results show that the new sampling approach performs as well as or better than conventional variable density sampling and without need for time‐consuming parameter optimisation. Further, a bespoke sampling pattern is produced for each rock type. The novel undersampling strategy was employed to acquire 3D magnetic resonance images of a Ketton limestone rock at spatial resolutions of 35 and 17.6 µm. The ability of the k‐space sampling scheme produced using the new approach in enabling reconstruction of the pore space characteristics of the rock was then demonstrated by benchmarking against the pore space statistics obtained from high‐resolution µCT data. The MRI data acquired at 17.6 µm resolution gave excellent agreement with the pore size distribution obtained from the X‐ray microcomputed tomography dataset, while the pore coordination number distribution obtained from the MRI data was slightly skewed to lower coordination numbers. This approach provides a method of producing a k‐space undersampling pattern for MRI acquisition at a spatial resolution for which a fully sampled acquisition at that spatial resolution would be impractically long. The approach can be easily extended to other CS‐MRI techniques, such as spatially resolved flow and relaxation time mapping. Lay Description Magnetic resonance imaging (MRI) is widely used to study the microstructure of, and fluid transport phenomena in porous media relevant for engineering applications. A major application is the study of water and hydrocarbon transport in porous sedimentary rocks, which typically have pore sizes smaller than 100 µm. The spatial resolution of routine MRI acquisitions, however, is limited to several hundred µm due to the relatively low sensitivity of the magnetic resonance method. Therefore, there exists a strong motivation to increase the spatial resolution of MRI by one to two orders of magnitude to be able to study these rocks at a pore scale. This work reports the in...

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Comparison of Voxel‐Based and Mesh‐Based CFD Models for Aerosol Deposition on Complex Fibrous Filters

Hoch

Azimian²,

Baumann

et al. 2020

Chem Eng & Technol

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Aerosols represent a health risk since small droplets may enter the respiratory system and cause lung cancer, allergies, or diseases like COVID-19. In this work, an Eulerian-Lagrangian computational fluid dynamics model is used based on a voxel-based (GeoDict) and a mesh-based (StarCCM+) code. For evaluating accuracy and computational time of both models, fractional filtration efficiency and pressure drop are compared to an empirical solution for a single fiber and to experimental results for a complex 3D fibrous filter material. Simulation results of both methods are in good agreement with empirical and measurement results although the complex geometry of the fibers is captured more accurately by the unstructured mesh using the same resolution. Computing times are much faster using the voxel-based code.

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References and benchmarks for pore-scale flow simulated using micro-CT images of porous media and digital rocks

Cited by 118 publications

References 72 publications

Lattice Boltzmann models for micro-tomographic pore-spaces

Lattice Boltzmann models for micro-tomographic pore-spaces

Identification of sampling patterns for high‐resolution compressed sensing MRI of porous materials: ‘learning’ from X‐ray microcomputed tomography data

Comparison of Voxel‐Based and Mesh‐Based CFD Models for Aerosol Deposition on Complex Fibrous Filters

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