ORCID Profile
0000-0002-3876-9160
Current Organisation
CSIRO
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Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 06-2022
Publisher: Wiley
Date: 03-03-2021
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 05-2022
Publisher: Elsevier BV
Date: 2016
Publisher: American Physical Society (APS)
Date: 21-08-2015
Publisher: American Geophysical Union (AGU)
Date: 04-2018
DOI: 10.1029/2017WR022282
Publisher: Elsevier BV
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 29-05-2023
Publisher: Springer Science and Business Media LLC
Date: 23-08-2022
DOI: 10.1007/S11242-022-01842-Z
Abstract: X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise the pore-scale geometry of subsurface porous rocks. Recent developments in super-resolution (SR) methods using deep learning allow for the digital enhancement of low-resolution (LR) images over large spatial scales, creating SR images comparable to high-resolution (HR) ground truth images. This circumvents the common trade-off between resolution and field-of-view. An outstanding issue is the use of paired LR and HR data, which is often required in the training step of such methods but is difficult to obtain. In this work, we rigorously compare two state-of-the-art SR deep learning techniques, using both paired and unpaired data, with like-for-like ground truth data. The first approach requires paired images to train a convolutional neural network (CNN), while the second approach uses unpaired images to train a generative adversarial network (GAN). The two approaches are compared using a micro-CT carbonate rock s le with complicated micro-porous textures. We implemented various image-based and numerical verifications and experimental validation to quantitatively evaluate the physical accuracy and sensitivities of the two methods. Our quantitative results show that the unpaired GAN approach can reconstruct super-resolution images as precise as the paired CNN method, with comparable training times and dataset requirements. This unlocks new applications for micro-CT image enhancement using unpaired deep learning methods image registration is no longer needed during the data processing stage. Decoupled images from data storage platforms can be exploited to train networks for SR digital rock applications. This opens up a new pathway for various applications related to multi-scale flow simulations in heterogeneous porous media.
Publisher: Wiley
Date: 08-01-2019
Publisher: American Geophysical Union (AGU)
Date: 30-05-2020
DOI: 10.1029/2019WR026396
Publisher: EDP Sciences
Date: 2019
DOI: 10.1051/E3SCONF/20198902001
Abstract: Incorporating mm-m scale capillary pressure heterogeneity into upscaled numerical models is key to the successful prediction of low flow potential plume migration and trapping at the field scale. Under such conditions, the upscaled, equivalent relative permeability incorporating capillary pressure heterogeneity is far from that derived conventionally at the viscous limit, dependent on the heterogeneity structure and flow rate, i.e. dependent on the capillary number. Recent work at the SCA 2017 symposium (SCA2017-022) demonstrated how equivalent functions can be obtained on heterogeneous rock cores from the subsurface under drainage conditions going beyond traditional SCAL. Experimental observations using medical CT scanning can be combined with numerical modelling so that heterogeneous subsurface rock cores can be directly characterized and used to populate field scale reservoir models. In this work, we extend this characterization approach by incorporating imbibition cycles into the methodology. We use a Bunter sandstone core with several experimental CO 2 – Brine core flood datasets at different flow rates (2x drainage, 1x imbibition and 2x trapping) to demonstrate the characterization of hysteretic multiphase flow functions in water-wet rocks. We show that mm-m scale experimental saturations and equivalent, low flow potential relative permeabilities can be predicted during drainage and imbibition, along with trapping characteristics. Equivalent imbibition relative permeabilities appear as a function of capillary number, as in the drainage cases. We also find that the form of capillary pressure function during imbibition has a large impact on the trapping characteristics, with local heterogeneity trapping reduced (or removed), if the capillary pressure drops to zero, or below at the residual saturation.
Publisher: American Geophysical Union (AGU)
Date: 09-2021
DOI: 10.1029/2021WR030581
Abstract: The characterization of multiphase flow properties is essential for predicting large‐scale fluid behavior in the subsurface. Insufficient representation of small‐scale heterogeneities has been identified as a major gap in conventional reservoir simulation workflows. We systematically evaluated the workflow developed by Jackson et al. (2018), 0.1029/2017wr022282 for use on rocks with complex porosity and capillary heterogeneities. The workflow characterizes capillary heterogeneity at the millimeter scale. The method is a numerical history match of a coreflood experiment with the 3D saturation distribution as a matching target and the capillary pressure characteristics as a fitting parameter. Coreflood experimental datasets of five rock cores with distinct heterogeneities were analyzed: two sandstones and three carbonates. The sandstones exhibit laminar heterogeneities. The carbonates have isotropic heterogeneities at a range of length scales. We found that the success of the workflow is primarily governed by the extent to which heterogeneous structures are resolved in the X‐ray imagery. The performance of the characterization workflow systematically improved with increasing characteristic length scales of heterogeneities. Using the validated models, we investigated the flow rate dependency of the upscaled relative permeability. The findings showed that the isotropic heterogeneity in the carbonate s le resulted in non‐monotonic behavior initially the relative permeability increased, and then subsequently decreased with increasing flow rate. The work underscores the importance of capturing small‐scale heterogeneities in characterizing subsurface fluid flows, as well as the challenges in doing so.
Publisher: Elsevier BV
Date: 06-2017
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 10-2017
Publisher: American Geophysical Union (AGU)
Date: 11-09-2020
DOI: 10.1029/2020GL088616
Abstract: Unpredicted, rapid plume elongation has been observed at subsurface CO 2 storage projects worldwide, exemplified by the Sleipner project. We show that conventionally ignored centimeter‐meter scale heterogeneity in capillary pressure characteristics can manifest as rapid field‐scale, decameter‐kilometer, plume migration. We analyze the effect in the Goldeneye field, UK, a proposed storage site with a unique combination of s le/data accessibility and generality as an archetype sandstone reservoir. We overcome previous barriers by characterizing in greater detail over larger scales—the 65 m reservoir height at cm‐m resolution—and through use of an upscaling scheme which resolves small‐scale heterogeneity impacts in field‐scale simulations. These models reveal that significant early time retardation of buoyantly rising CO 2 plumes is followed by rapid migration under the caprock in the presence of anisotropic, layered heterogeneities. Lateral migration speeds can be enhanced by 200%, placing first‐order controls on fluid flow and providing a mechanistic explanation for field observations.
Publisher: American Geophysical Union (AGU)
Date: 30-05-2020
DOI: 10.1029/2019WR026708
Publisher: American Physical Society (APS)
Date: 27-05-2022
Publisher: California Digital Library (CDL)
Date: 21-09-2019
Publisher: American Geophysical Union (AGU)
Date: 04-2021
DOI: 10.1029/2020WR028597
Abstract: Carbonate rock reservoirs are dominated by heterogeneity across a large and continuous range of spatial scales. We study the impact of heterogeneities on relative permeability and residual trapping for three carbonate rocks selected for their distinct spatial scales of rock texture. The Indiana limestone comprises millimeter‐scale heterogeneities, the Estaillades limestone consists of half‐centimeter‐scale heterogeneities, and the Edwards dolomite includes decimeter‐scale heterogeneity. Along with routine characterization of rock s les, steady‐state N 2 –deionized water drainage relative permeability measurements are made for each rock at two distinct total flow rates, at least 1 order of magnitude apart. The variation in flow potential across the core results in observations of fluid distribution, core‐average relative permeability, and residual trapping obtained for a range of continuum‐scale capillary number . The relative permeability curves for all rocks shift to the right of the water saturation axis with increasing flow potential the nitrogen relative permeability increases while the water relative permeability decreases. However, the magnitude of the shift depends on the spatial scale of heterogeneity. An inspection of 3D saturation distributions in the cores and estimation of the capillary numbers of flow shows that the rock with the largest heterogeneity is capillary flow dominated throughout the range of injection rates tested observations in the Indiana and Estaillades carbonates traverse capillary to viscous dominated flow regimes, with commensurate flow rate dependence in the relative permeability. In all cases, residual trapping is poorly described by the Land model.
Publisher: American Geophysical Union (AGU)
Date: 18-06-2019
DOI: 10.1029/2019GL082738
Publisher: Wiley
Date: 25-03-2015
DOI: 10.1002/FLD.4028
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
No related grants have been discovered for Samuel Jackson.