Multiscale Study of Chemically-tuned Waterflooding in Carbonate Rocks using Micro-Computed Tomography

Tawfik, Miral S.; Karpyn, Zuleima T.; Johns, Russell T.

doi:10.3997/2214-4609.201900074

Cited by 14 publications

(6 citation statements)

References 24 publications

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“…X-ray imaging has been previously used to study LSW [49][50][51][52][53], with only a few studies on carbonate rocks [27,[54][55][56]. Using micro-CT imaging, Qin et al [57] compared high and low salinity flooding at reservoir conditions.…”

Section: Introductionmentioning

confidence: 99%

Pore-scale processes in tertiary low salinity waterflooding in a carbonate rock: Micro-dispersions, water film growth, and wettability change

Selem

Agenet

Blunt

et al. 2022

Journal of Colloid and Interface Science

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Section: Introductionmentioning

confidence: 99%

Pore-scale processes in tertiary low salinity waterflooding in a carbonate rock: Micro-dispersions, water film growth, and wettability change

Selem

Agenet

Blunt

et al. 2022

Journal of Colloid and Interface Science

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“…This core holder is made of an aluminum body wrapped in carbon fiber composite, which reduces the holder's X-ray absorption capacity during scanning. The porous medium is saturated with high salinity brine that has a composition representative of a saline aquifer (Tawfik et al, 2019). Brine was pre-equilibrated with CO 2 at 1,500 psi and 45 • C before being used to saturate the core.…”

Section: Methodology Image Acquisitionmentioning

confidence: 99%

“…They characterized the 3D pore structure in a glass bead pack and three Berea sandstone samples to determine whether topological properties such as pore connectivity and phase features of individual fluid phases such as saturation can be resolved. Micro-computed tomography has since been successfully used for quantifying a wide range of petrophysical properties such as volume fraction for porosity and saturation quantification, specific surface area (SSA), pore-and blob-size distributions, in-situ contact angles, interface curvatures for local capillary pressures, grain sphericity, angularity, roughness as well as phase connectivity (Sharma and Yortsos, 1987;Prodanović and Bryant, 2006;Karpyn et al, 2010;Herring et al, 2013;Landry et al, 2014;Larpudomlert et al, 2014;Berg et al, 2016;Klise et al, 2016;Scanziani et al, 2017;Chen et al, 2018;Tawfik et al, 2019;McClure et al, 2020). The accuracy with which we can estimate these pore-scale properties affect our ability to explain and predict multiphase fluid flow in porous media.…”

Section: Background and Introductionmentioning

confidence: 99%

Comparative Study of Traditional and Deep-Learning Denoising Approaches for Image-Based Petrophysical Characterization of Porous Media

et al. 2022

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Digital rock physics has seen significant advances owing to improvements in micro-computed tomography (MCT) imaging techniques and computing power. These advances allow for the visualization and accurate characterization of multiphase transport in porous media. Despite such advancements, image processing and particularly the task of denoising MCT images remains less explored. As such, selection of proper denoising method is a challenging optimization exercise of balancing the tradeoffs between minimizing noise and preserving original features. Despite its importance, there are no comparative studies in the geoscience domain that assess the performance of different denoising approaches, and their effect on image-based rock and fluid property estimates. Further, the application of machine learning and deep learning-based (DL) denoising models remains under-explored. In this research, we evaluate the performance of six commonly used denoising filters and compare them to five DL-based denoising protocols, namely, noise-to-clean (N2C), residual dense network (RDN), and cycle consistent generative adversarial network (CCGAN)—which require a clean reference (ground truth), as well as noise-to-noise (N2N) and noise-to-void (N2V)—which do not require a clean reference. We also propose hybrid or semi-supervised DL denoising models which only require a fraction of clean reference images. Using these models, we investigate the optimal number of high-exposure reference images that balances data acquisition cost and accurate petrophysical characterization. The performance of each denoising approach is evaluated using two sets of metrics: (1) standard denoising evaluation metrics, including peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR), and (2) the resulting image-based petrophysical properties such as porosity, saturation, pore size distribution, phase connectivity, and specific surface area (SSA). Petrophysical estimates show that most traditional filters perform well when estimating bulk properties but show large errors for pore-scale properties like phase connectivity. Meanwhile, DL-based models give mixed outcomes, where supervised methods like N2C show the best performance, and an unsupervised model like N2V shows the worst performance. N2N75, which is a newly proposed semi-supervised variation of the N2N model, where 75% of the clean reference data is used for training, shows very promising outcomes for both traditional denoising performance metrics and petrophysical properties including both bulk and pore-scale measures. Lastly, N2C is found to be the most computationally efficient, while CCGAN is found to be the least, among the DL-based models considered in this study. Overall, this investigation shows that application of sophisticated supervised and semi-supervised DL-based denoising models can significantly reduce petrophysical characterization errors introduced during the denoising step. Furthermore, with the advancement of semi-supervised DL-based models, requirement of clean reference or ground truth images for training can be reduced and deployment of fast X-ray scanning can be made possible.

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“…115 To understand the effect of wettability on oil recovery, the residual oil saturation can be expressed in terms of a wettability index (i.e., USBM or Amott wettability indices). Tawfik et al 116 compiled previously published data to demonstrate the relationship between the wettability index and the residual oil saturation. The data show the adverse effect on oil recovery in the extreme wetting states (i.e., U-shaped plots).…”

Section: Interfacial Tension Reductionmentioning

confidence: 99%

Oil Recovery Performance by Surfactant Flooding: A Perspective on Multiscale Evaluation Methods

et al. 2022

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Chemical-enhanced oil recovery (cEOR) is a class of techniques commonly used to extract hydrocarbon fluids from reservoir rocks beyond conventional waterflooding. Surfactants are among the chemical agents employed in a cEOR process, as they aid in enhancing oil recovery by lowering the oil–water interfacial tension (IFT) and altering the rock wettability toward less oil-wet conditions. Understanding the flow characteristics and mechanisms involved during surfactant flooding helps improve the performance of injected surfactants and results in higher oil recovery. The objective of this review is to outline the recent applications of the different methods employed to understand the behavior and mechanisms involved during surfactant-enhanced oil recovery. The review begins with a general background highlighting the basic characteristics of surfactants and the main mechanisms by which they exert their influence. Recent studies conducted to investigate the oil recovery performance through different methods are then presented, including traditional coreflooding experiments, microfluidics studies, and oil recovery through sand packs. The methodology of the analysis and the interpretation of the data obtained from the different oil recovery tests, including oil recovery factor, pressure data, and relative permeability, are also described. Pore-scale analysis and imaging methods including nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), and X-ray medical and microcomputed tomography (μCT) scanning and their applicability in assessing the recovery performance are described. Finally, a few examples of field monitoring methods for surfactant flooding are highlighted. This review provides knowledge of the different multiscale evaluation methods and their applicability during surfactant flooding.

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Multiscale Study of Chemically-tuned Waterflooding in Carbonate Rocks using Micro-Computed Tomography

Cited by 14 publications

References 24 publications

Pore-scale processes in tertiary low salinity waterflooding in a carbonate rock: Micro-dispersions, water film growth, and wettability change

Pore-scale processes in tertiary low salinity waterflooding in a carbonate rock: Micro-dispersions, water film growth, and wettability change

Comparative Study of Traditional and Deep-Learning Denoising Approaches for Image-Based Petrophysical Characterization of Porous Media

Oil Recovery Performance by Surfactant Flooding: A Perspective on Multiscale Evaluation Methods

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