Accelerated Computation of Relative Permeability by Coupled Morphological and Direct Multiphase Flow Simulation

Wang, Ying Da; Chung, Traiwit; Armstrong, Ryan T.; McClure, James E.; Ramstad, T.A.; Mostaghimi, Peyman

doi:10.1016/j.jcp.2019.108966

Cited by 39 publications

(23 citation statements)

References 50 publications

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“…where I is the number of isolated objects, L is the number of redundant loops, and O is the number of cavities (Armstrong et al, 2019). Pore size distribution is estimated by a local distance maximum method (Chung et al, 2020;Shabro et al, 2012;Y. D. Wang, Chung, et al, 2020), which is similar to a watershed-distance transform (Strahler, 1957) or the maximum inscribed spheres method (Silin & Patzek, 2006).…”

Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

“…The Euler characteristic ( χ ) for a digital image ( X ) is expressed as

χ ((), X) = I - L +

where I is the number of isolated objects, L is the number of redundant loops, and O is the number of cavities (Armstrong et al, 2019). Pore size distribution is estimated by a local distance maximum method (Chung et al, 2020; Shabro et al, 2012; Y. D. Wang, Chung, et al, 2020), which is similar to a watershed‐distance transform (Strahler, 1957) or the maximum inscribed spheres method (Silin & Patzek, 2006). Lastly, the corresponding absolute permeability is calculated based on the single phase Lattice‐Boltzmann method (McClure et al, 2014; Y. D. Wang, Chung, et al, 2020).…”

Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

“…Pore size distribution is estimated by a local distance maximum method (Chung et al, 2020; Shabro et al, 2012; Y. D. Wang, Chung, et al, 2020), which is similar to a watershed‐distance transform (Strahler, 1957) or the maximum inscribed spheres method (Silin & Patzek, 2006). Lastly, the corresponding absolute permeability is calculated based on the single phase Lattice‐Boltzmann method (McClure et al, 2014; Y. D. Wang, Chung, et al, 2020).…”

Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

See 2 more Smart Citations

An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

Niu

Wang

Mostaghimi

et al. 2020

Geophysical Research Letters

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High-resolution X-ray microcomputed tomography (micro-CT) data are used for the accurate determination of rock petrophysical properties. High-resolution data, however, result in a small field of view, and thus, the representativeness of a simulation domain can be brought into question when dealing with geophysical applications. This paper applies a cycle-in-cycle generative adversarial network (CinCGAN) to improve the resolution of 3-D micro-CT data and create a super-resolution image using unpaired training images. Effective porosity, Euler characteristic, pore size distribution, and absolute permeability are measured on super-resolution and high-resolution ground-truth images to evaluate the physical accuracy of the proposed CinCGAN. The results demonstrate that CinCGAN provides physically accurate images with an order of magnitude larger field of view when compared to typical micro-CT methods. This unlocks new pathways for the geophysical characterization of subsurface rocks with broad implications for flow modeling in highly heterogeneous rocks or fundamental studies on nonlocal forces that extend beyond domain sizes typically used for pore-scale simulation. Plain Language Summary Digital rock analysis aims to characterize the physical structure of porous rocks with the aid of X-ray computed microtomography for the simulation of effective properties. The accuracy of these simulations to actual physical rock properties is highly dependent on how well the rock pores are resolved in addition to the particular setup and solvers used. We expect to obtain accurate results from high-resolution images with a large enough field of view to fully characterize microstructural heterogeneities given an appropriate numerical scheme. However, high-resolution data are obtained at the cost of a smaller field of view due to technological limitations. To circumvent this issue, super-resolution methods based on deep learning algorithms can be applied to improve image resolution by learning the mappings between low-and high-resolution data. The training data, however, often require the pairing of low-/high-resolution images, which is obtained by a time-consuming process of image registration. We apply a novel deep learning algorithm to achieve 3-D super-resolution images using unpaired training data. We demonstrate that super-resolution data can provide physically accurate rock images compared to high-resolution ground-truth images. The presented method provides new opportunities to study core-scale properties, such as heterogeneity and flow dynamics, at a resolution high enough to resolve the microscopic rock structure.

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Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

“…The Euler characteristic ( χ ) for a digital image ( X ) is expressed as

χ ((), X) = I - L +

Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

Section: Image Processing and Petrophysical Analysismentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

Niu

Wang

Mostaghimi

et al. 2020

Geophysical Research Letters

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“…For instance, relative permeability, which plays a key role in reservoir characterization and production, is measured in the laboratory using either steady-or unsteady-state methods, which are lengthy and costly. These experimental methods involve injecting different phases into core samples and measuring flow rates [1][2][3][4][5]. Measuring relative permeability in the laboratory also requires a large number of high-quality core samples from a targeted reservoir [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…These experimental methods involve injecting different phases into core samples and measuring flow rates [1][2][3][4][5]. Measuring relative permeability in the laboratory also requires a large number of high-quality core samples from a targeted reservoir [5,6].…”

Section: Introductionmentioning

confidence: 99%

Pore-Scale Characterization and PNM Simulations of Multiphase Flow in Carbonate Rocks

et al. 2021

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This paper deals with pore-scale two-phase flow simulations in carbonate rock using the pore network method (PNM). This method was used to determine the rock and flow properties of three different rock samples, such as porosity, capillary pressure, absolute permeabilities, and oil–water relative permeabilities. The pore network method was further used to determine the properties of rock matrices, such as pore size distribution, topological structure, aspect ratio, pore throat shape factor, connected porosity, total porosity, and absolute permeability. The predicted simulation for the network-connected porosity, total porosity, and absolute permeability agree well with those measured experimentally when the image resolution is appropriate to resolve the relevant pore and throat sizes. This paper also explores the effect of the wettability and fraction of oil-wet pores on relative permeabilities, both in uniform and mixed wet systems.

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The LBPM software package for simulating multiphase flow on digital images of porous rocks

McClure

Berrill

et al. 2021

Comput Geosci

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Accelerated Computation of Relative Permeability by Coupled Morphological and Direct Multiphase Flow Simulation

Cited by 39 publications

References 50 publications

An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

An Innovative Application of Generative Adversarial Networks for Physically Accurate Rock Images With an Unprecedented Field of View

Pore-Scale Characterization and PNM Simulations of Multiphase Flow in Carbonate Rocks

The LBPM software package for simulating multiphase flow on digital images of porous rocks

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