Ultrahigh-Resolution 3D Imaging for Quantifying the Pore Nanostructure of Shale and Predicting Gas Transport

Garum, Mohamed; Glover, Paul; Lorinczi, Piroska; Scott, Gilbert; Hassanpour, Ali

doi:10.1021/acs.energyfuels.0c03225

Cited by 13 publications

(27 citation statements)

References 68 publications

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“…In this study, we perform and present the nano-CT measurements and the supporting MIP and nitrogen adsorption data. The nano-CT data was measured using a ZEISS Ultra Nano-CT instrument at the University of Manchester (as outlined in [23]). Measurements were made on a cuboid sample (Figure 2b) with a side length of approximately 25 µm and a voxel size of approximately 64x64x64 nm 3 .…”

Section: Multi-scale 2d/3d Imaging Techniquesmentioning

confidence: 99%

“…The data from all 2D/3D imaging techniques were analysed using a protocol of image processing techniques such as filtering, thresholding, segmentation and quantification [17,23]. This process was carried out using Avizo®9.0 software and resulted in a wide range of numerical measurements including pore size, pore volume, pore surface area, and pore dimensions, for each recognised pore.…”

Section: D/3d Imaging Analysismentioning

confidence: 99%

“…The pore surface area to volume ratio (ξ) is another important parameter for describing the shape of the pore within shale reservoir [17,19,23]. This ratio is very significant particularly in shale because large surface areas help the gas move more easily from the rock matrix and from kerogen into the pores, which is a pre-requisite to hydraulic fracturing because gas must be able to transfer into the existing small pore spaces before stimulation can improve the connectivity of the small pore spaces sufficiently for gas to be produced.…”

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

“…We do not measure values in this range. The reason is that the simple surface area to volume ratio is scale dependent and the calculations that are carried out in Figure 8 are for a native scale of a=1 m. In order to overcome this problem a scale invariant surface area to volume ratio has been proposed [23].…”

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

“…Figure 9 shows the distributions of number of pores (blue data) and volume of pores (red data), both expressed as a percentage, as a function of the scale-invariant surface area to volume ratio,  [23] for the nano-CT data presented in this paper. The mathematical definition of the scale-invariant surface area to volume ratio is such that only positive values greater than 3 are possible, representing a spherical pore.…”

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

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Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

et al. 2021

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Quantification of the microstructures of shales is difficult due to their complexity which extends across many orders of magnitude of scale. Nevertheless, shale microstructures are extremely important, not only as shale gas resources, but as cap-rocks in CCS resources, in geothermal reservoirs and as a host to the long-term storage of radioactive materials. In this work, we have carried out ultra-high resolution CT imaging (nano-CT), mercury injection porosimetry (MIP) and nitrogen adsorption experiments on a sample of gas shale for which we already have focussed ion beam scanning electron microscopy (FIB-SEM) and high resolution CT (micro-CT) datasets. The combination of these datasets has allowed us to examine the microstructure of the shale in unprecedented depth across a wide range of scales (from about 20 nm to 0.5 mm). Overall the sample shows a porosity of 0.67±0.009% from the nano-CT data, 0.0235±0.003% from nitrogen adsorption, and 0.60±0.07% from MIP, which compare with 0.10±0.01%, 0.52±0.05%, 0.94±0.09% from 3 FIB-SEM measurements and 0.06±0.008% from one micro-CT measurements The data vary due to the different scales at which each technique interrogates the rock and whether the pores are openly accessible (especially in the case of the nitrogen adsorption value). Measured kerogen fraction is 32.4±1.45% from nano-CT, compared with 34.8±1.74%, 38.2±1.91%, 41.4±2.07%, and 44.5±2.22% for 3 FIB-SEM and one micro-CT measurement. The pore size imaged by nano-CT ranged between 100 nm to 5000 nm, while the corresponding ranges were between 3 and 2000 nm for MIP analysis and between 2 nm to 90 nm for N2 adsorption. The distribution of pore aspect ratio and scale-invariant pore surface area to volume ratio (σ) as well as the calculated permeability shows the sample to have a high shale gas potential. Aspect ratios indicate that most of the pores which contribute significantly to pore volume are oblate, which is confirmed by the range of σ (3 to 13). Oblate pores have greater potential for interacting with other pores compared to equant and needle-shaped prolate pores, as well optimising surface area for gas to desorb from the kerogen into the pores. Permeability essays provide 2.61±0.42 nD from the nano-CT data, 2.65±0.45 nD from MIP, and (5.07±0.02) ×10 -4 nD from nitrogen adsorption, which are consistent with expectations for generic gas shales (i.e., tens of nD) and the measurements made previously on the same sample using FIB-SEM and micro-CT imaging techniques.

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Section: Multi-scale 2d/3d Imaging Techniquesmentioning

confidence: 99%

Section: D/3d Imaging Analysismentioning

confidence: 99%

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

Section: Pore Surface Area To Volume Ratiomentioning

confidence: 99%

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Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

et al. 2021

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Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Angelidakis

Nadimi

Garum

et al. 2022

Part & Part Syst Charact

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This study analyses the morphology of iron pyrite particles within a shale sample, captured using nano-computed tomography (Nano-CT). The complex, framboidal morphology of the iron pyrite particles is characterised using various metrics, and comparisons are drawn on their effectiveness to quantify their observed morphological characteristics. Then, simplified representations of selected iron pyrite particles are generated to facilitate a sensitivity analysis of the effect of imaging resolution on morphological parameters of particle form. A discussion is developed on the required number of pixels per particle diameter for particle shape characterisation. It is shown that shape indices that rely on the simplified main particle dimensions can be accurately calculated even for low fidelity levels of 10 pixels per particle diameter. More complex shape indices, that use vertices, volume and surface area, are more sensitive to image resolution even for 40 pixels per particle diameter.

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Pore structure characteristics of reservoirs of Xihu Sag in East China Sea Shelf Basin based on dual resolution X-ray computed tomography and their influence on permeability

Zeng

Dong

Chen

et al. 2022

Energy

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Ultrahigh-Resolution 3D Imaging for Quantifying the Pore Nanostructure of Shale and Predicting Gas Transport

Abstract: This is a repository copy of Ultrahigh-Resolution 3D Imaging for Quantifying the Pore Nanostructure of Shale and Predicting Gas Transport.

Cited by 13 publications

References 68 publications

Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Pore structure characteristics of reservoirs of Xihu Sag in East China Sea Shelf Basin based on dual resolution X-ray computed tomography and their influence on permeability

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