Dual-scale pore network reconstruction of vugular carbonates using multi-scale imaging techniques

Moslemipour, Abolfazl; Sadeghnejad, Saeid

doi:10.1016/j.advwatres.2020.103795

Cited by 23 publications

(4 citation statements)

References 58 publications

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“…Figure 1 depicts the general workflow in this study. First, CT scanning images [52,53] were converted to binary series using an image processing procedure via ImageJ, an opensource platform [54] (Fig. 2a).…”

Section: Methods and Materials 21 From Computed Tomography To Proxy Modelmentioning

confidence: 99%

Evaluation of 3D printed microfluidic networks to study fluid flow in rocks

Mousavi

Sadeghnejad

Ostadhassan

2021

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

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Visualizing fluid flow in porous media can provide a better understanding of transport phenomena at the pore scale. In this regard, transparent micromodels are suitable tools to investigate fluid flow in porous media. However, using glass as the primary material makes them inappropriate for predicting the natural behavior of rocks. Moreover, constructing these micromodels is time-consuming via conventional methods. Thus, an alternative approach can be to employ 3D printing technology to fabricate representative porous media. This study investigates fluid flow processes through a transparent microfluidic device based on a complex porous geometry (natural rock) using digital-light processing printing technology. Unlike previous studies, this one has focused on manufacturing repeatability. This micromodel, like a custom-built transparent cell, is capable of modeling single and multiphase transport phenomena. First, the tomographic data of a carbonate rock sample is segmented and 3D printed by a digital-light processing printer. Two miscible and immiscible tracer injection experiments are performed on the printed microfluidic media, while the experiments are verified with the same boundary conditions using a CFD simulator. The comparison of the results is based on Structural Similarity Index Measure (SSIM), where in both miscible and immiscible experiments, more than 80% SSIM is achieved. This confirms the reliability of printing methodology for manufacturing reusable microfluidic models as a promising and reliable tool for visual investigation of fluid flow in porous media. Ultimately, this study presents a novel comprehensive framework for manufacturing 2.5D realistic microfluidic devices (micromodels) from pore-scale rock images that are validated through CFD simulations.

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Section: Methods and Materials 21 From Computed Tomography To Proxy Modelmentioning

confidence: 99%

Evaluation of 3D printed microfluidic networks to study fluid flow in rocks

Mousavi

Sadeghnejad

Ostadhassan

2021

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

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“…The result is a system where the relevant fluid dynamics can be readily solved through idealized equations for flow (Dong and Blunt, 2009;Joekar-Niasar and Hassanizadeh, 2012;Jiang et al, 2012;Blunt et al, 2013;Huang et al, 2016;Suo et al, 2020). In D-PNMs, the presence of SRP is accounted for through the implementation of an additional finescale pore network (Ioannidis and Chatzis, 2000;Prodanović et al, 2014;Sadeghnejad and Gostick, 2020;Moslemipour and Sadeghnejad, 2020) or through the creation of "micro-links" forming percolation paths between large pores (Bultreys et al, 2015;Xu et al, 2021). Accurate definition of SRP connectivity within these networks remains a challenge (Zhao et al, 2017;Petrovskyy et al, 2020), as, by definition, there are no discernible features from which to inform assignments of pore network topology within the SRP.…”

Section: Multiscale Modelsmentioning

confidence: 99%

The Impact of Sub-Resolution Porosity on Numerical Simulations of Multiphase Flow

Carrillo,

Soulaine,

Bourg

2021

Preprint

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Sub-resolution porosity (SRP) is an ubiquitous, yet often ignored, feature in Digital Rock Physics. It embodies the trade-off between image resolution and field-of-view, and it is a direct result of choosing an imaging resolution that is larger than the smallest pores in a heterogeneous rock sample. In this study, we investigate the impacts of SRP on multiphase flow in porous rocks. To do so, we use our newly developed Multiphase Micro-Continuum model to perform first-of-a-kind direct numerical simulations of two-phase flow in porous samples containing SRP. We show that SRP properties (porosity, permeability, wettability) can impact predicted absolute permeabilities, fluid breakthrough times, residual saturations, and relative permeabilities by factors of up to 2, 1.5, 3, and 20, respectively. In particular, our results reveal that SRP can function as a persistent connector preventing the formation of isolated wetting fluid domains during drainage, thus dramatically increasing relative permeabilities to both fluids at low saturations. Overall, our study confirms previous evidence that the influence of SRP cannot be disregarded without incurring significant errors in numerical predictions or experimental analyses of multiphase flow in heterogeneous porous media.

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“…Increasing computational power and advances in interdisciplinary scientific fields such as water resources research allowed DRP to become a key tool in studying fractured media (Andrä et al 2013a, b). This approach is now used in many applications, including contaminant transport carbon capture and storage, enhanced oil recovery, tight gas production, reactive flow (Iassonov et al 2009;Blunt et al 2013;Blöcher et al 2019;Hinz et al 2019;Stoll et al 2019;Zhang et al 2019;Moslemipour and This article is a part of the Topical Collection in Environmental Earth Sciences on "Sustainable Utilization of Geosystems" guest edited by Ulf Hünken, Peter Dietrich and Olaf Kolditz. Sadeghnejad 2021).…”

Section: Introductionmentioning

confidence: 99%

“…This approach is now used in many applications, including contaminant transport carbon capture and storage, enhanced oil recovery, tight gas production, reactive flow (Iassonov et al 2009;Blunt et al 2013;Blöcher et al 2019;Hinz et al 2019;Stoll et al 2019;Zhang et al 2019;Moslemipour and This article is a part of the Topical Collection in Environmental Earth Sciences on "Sustainable Utilization of Geosystems" guest edited by Ulf Hünken, Peter Dietrich and Olaf Kolditz. Sadeghnejad 2021). A typical DRP workflow consists of three steps: (i) image acquisition and processing, (ii) image segmentation (i.e., categorizing each voxel to a specific phase, like pore or matrix), and (iii) numerical simulations (Andrä et al 2013a, b).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

et al. 2022

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Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting the analysis of physical rock properties. Conventional segmentation techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates the advantages of using the random forest (RF) algorithm for the segmentation of fractured rocks. The segmentation quality is discussed and compared with two conventional image processing methods (thresholding-based and watershed algorithm) and an encoder–decoder network in the form of convolutional neural networks (CNNs). The segmented images of the RF method were used as the ground truth for CNN training. The images of two fractured rock samples are acquired by X-ray computed tomography scanning (XCT). The skeletonized 3D images are calculated, providing information about the mean mechanical aperture and roughness. The porosity, permeability, flow fields, and preferred flow paths of segmented images are analyzed by the DRP approach. Moreover, the breakthrough curves obtained from tracer injection experiments are used as ground truth to evaluate the segmentation quality of each method. The results show that the conventional methods overestimate the fracture aperture. Both machine learning approaches show promising segmentation results and handle all artifacts and complexities without any prior CT-image filtering. However, the RF implementation has superior inherent advantages over CNN. This method is resource-saving (e.g., quickly trained), does not need an extensive training dataset, and can provide the segmentation uncertainty as a measure for evaluating the segmentation quality. The considerable variation in computed rock properties highlights the importance of choosing an appropriate segmentation method.

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Dual-scale pore network reconstruction of vugular carbonates using multi-scale imaging techniques

Cited by 23 publications

References 58 publications

Evaluation of 3D printed microfluidic networks to study fluid flow in rocks

Evaluation of 3D printed microfluidic networks to study fluid flow in rocks

The Impact of Sub-Resolution Porosity on Numerical Simulations of Multiphase Flow

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

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