The permeability of rocks is important in a range of geoscientific applications, including CO_2 sequestration, geothermal energy extraction, and in situ mineral recovery. This work presents an investigation of the change in permeability in porphyry rock samples due to blast-induced fracturing. Two samples were analysed before and after exposure to stress waves induced by the detonation of an explosive charge. Micro-computed tomography was used to image the interior of the samples at a pixel resolution of 10.3 μ m . The images were segmented into void, matrix, and grain to help quantify the differences in the rock samples. Following this, they were binarised as void or solid and the cumulant lattice Boltzmann method (LBM) was applied to simulate the flow of fluid through the connected void space. A correction required with the use of inlet and outlet reservoirs in computational permeability assessment was also proposed. Interrogation of the steady-state flow field allowed the pre- and post-loading permeability to be extracted. Conclusions were then drawn as to the effectiveness of blasting for enhancing fluid accessibility via the generation of microfractures in the rock matrix within the vicinity of a detonated charge. This paper makes contributions in three fundamental areas relating to the numerical assessment of permeability and the enhancement of fluid accessibility in low-porosity rocks. Firstly, a correction factor was proposed to account for the reservoirs commonly imposed on digitised rock samples when investigating sample permeability through numerical methods. Secondly, it validates the benefits of the LBM in handling complex geometries that would be intractable with conventional computational fluid dynamics methods that require body-fitted meshing. This is done with a novel implementation of the cumulant LBM in the open-source TCLB code. Finally, the improvement in fluid accessibility in low-permeability rock samples was shown through the assessment of multiple regions within two blasted samples. It was found that the blast-induced loading can generate extended microfractures that results in multiple orders of magnitude of permeability enhancement if the target rock possesses existing weaknesses and/or mineralisation.

X-ray computed tomography (XCT) is routinely used in geosciences for the purpose of rock characterisation. High-quality micro-CT images are successfully used for fracture characterisation, as well as analysis of grains and pores. In contrast, the use of XCT for mineral identification is uncommon and often ineffective. Implementation of micro-CT imaging techniques for mineral identification is affected by the accuracy and precision of the image segmentation results. Conventional segmentation methods such as thresholding, watershed, and active contouring are user-biased and do not provide the robust distinction between various heavy accessory minerals in granite rocks. Heavy ore minerals such as pyrite, chalcopyrite, molybdenite, and ilmenite are readily recognised in grey-scale micro-CT images because of their high attenuation coefficient, but further differentiation between these minerals using only traditional segmentation methods is challenging. Conversely, deep convolutional neural networks (CNNs) are fully self-trained, and they have demonstrated accurate semantic segmentation results for rock images. However, the application of CNN semantic segmentation for igneous rocks is not well documented. In this research, the U-Net 2.5D CNN was deployed to train the neural network on a combination of high-resolution micro-CT and mineral liberation analysis (MLA) images to identify different accessory mineral regions of interest (ROIs). The image segmentation results were assessed using MLA and SEM data, and the accuracy of segmentation was found to be greater than 97%. The methodology developed in this study can be extended to map the mineralogy of granite samples unseen by the CNN to further validate the robustness of the approach.

X-ray micro-computed tomography (micro-CT) is widely used for three-dimensional analysis of many rock types. However, the practical implementation of this method for micro-porous samples requires a compromise between the resolution of the images and the obtainable field of view (FOV). Generally, resolution enhancement results in a reduction of the FOV. The generation of high-quality micro-CT images is an expensive and time consuming task due to the competing requirements of a large FOV and fine resolution. To alleviate this, super-resolution processing, based on deep learning, is proposed to improve the quality of low-resolution images that can obtain a large FOV. In this research, a super-resolution technique employing the three-dimensional U-Net convolutional neural network (CNN) architecture was applied to enhance the resolution of granodiorite rock sample images. This was undertaken using two sets of micro-CT image triplexes, where the first triplex contained 3-, 6-, and 12-micron resolution sets, and the second triplex contained 1-, 2-, and 4-micron resolution sets. For each triplex, 80% of the images were used for training the neural network with the remaining 20% used for validation. Further validation was performed by comparing the processed results to images obtained from scanning electron microscopy (SEM). It was observed that super-resolution processing can significantly improve the low-resolution micro-CT image quality without physically reducing the sample size typically required for high-resolution scanning. It is expected that this technique could assist practitioners reveal features absent in small samples (e.g. large fractures and or rock textures). Furthermore, images restored through super-resolution processing maintain the FOV of the lower resolution scan, a task that would be comparatively expensive and time consuming to acquire in a high-resolution scan. The workflow proposed in this study has a significant impact on a range of fields including the numerical prediction of rock permeability, and segmentation for advanced mineral analysis.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Solid Earth. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Enhancement of Scanco micro-CT images of granodiorite rocks using a 3D convolutional neural network super-resolution algorithmAuthorsAlexandraRosliniDMaximLebedevTravis RyanMitchellItalo AndresOnederraChristopher RossLeonardiSee all authors Alexandra RosliniDCorresponding Author• Submitting AuthorUniversity of QueenslandiDhttps://orcid.org/0000-0002-7148-1628view email addressThe email was not providedcopy email addressMaxim LebedevCurtin Universityview email addressThe email was not providedcopy email addressTravis Ryan MitchellThe University of Queenslandview email addressThe email was not providedcopy email addressItalo Andres OnederraUniversity of Queenslandview email addressThe email was not providedcopy email addressChristopher Ross LeonardiThe University of Queenslandview email addressThe email was not providedcopy email address

This paper presents an analysis of the matrix pore size distribution and simulation of fluid flow in the coal matrix in intermediate-rank coal. The study used scanning electron microscopy images, nuclear magnetic resonance, and mercury injection capillary pressure (MICP) data, which were used to reconstruct the three-dimensional (3D) coal matrix model and analyze the distribution of pores in the coal matrix. The reconstructed 3D model of the coal matrix pore space was further used to simulate capillary-dominated two-phase flow for capillary pressure curves and fluid configuration calculation. The analysis showed that there is good congruence between the simulated and measured MICP curves, which could mean that the described simulation method could potentially be used for modeling the fluid flow in coal. A simulation approach, which was described in the paper, can potentially be implemented to model fluid flow in a dual-pore single-permeability or dual-pore dual-permeability model. Results confirm that the contribution of the coal matrix to the permeability and fluid flow is negligible as a result of the poor connectivity of the pore system in the coal matrix of the studied samples.

This paper comprises the analysis of scanning electron microscopy (SEM) images, nuclear magnetic resonance (NMR) and mercury injection capillary pressure (MICP) data to quantify the pore distribution in coal matrix. We first generate the 3D pore system in the coal matrix based on the statistics of pore distribution obtained from 2D SEM images, and then extract the pore network using the maximal ball method. The influence of the reconstructed cube size and the 2D image resolution on the accuracy of the reconstructed 3D coal sample was analysed when generate the 3D digital coal smaple. It was observed that the highest resolution which was achieved for the studied samples (6 nm) resulted in the underestimation of porosity of the studied sample, and it is recommended for future to create several models with different resolution to find the most representative model, instead of apriori using the highest possible resolution. The extracted pore network was then used to analyse pore size distribution and perform capillary pressure simulation using pore network modeling. A comparison of the pore network analysis with NMR and measured MICP data demonstrated that the pore network extraction method simplified the results of distribution and underestimated the size of elongated pores and microfractures. The simulated and laboratory measured MICP shows significant difference partically bucease the network extraction method was not suitable for the studied samples and this could be overcomed by our future study of model MICP using direct simulation method in the reconstructed 3D model.

Coal has been playing an important role as a valuable source of energy for many years. In turn, gas production from coal reservoirs is a modern development and coal bed methane (CBM), also known as coal seam gas (CSG), is attracting global attention due to its wide occurrence and benefits for the environment as opposed to the conventional energy sources. Developing coal bed methane reservoirs requires better understanding of the flow behaviours of gas and liquids in cleats and analysis of possible contribution of pores to the flow. This paper describes the implementation of micro computed tomography (micro-CT) and scan electron microscopy (SEM) techniques for analysis of coal samples. Intermediate rank coal samples used in this study were collected from Southern Qinshui Basin (China). In the course of the described research, coal samples were scanned, processed and segmented to study the cleat spacing and permeability. Due to the partial volume effect, the resolution of cleats needed improvement which was achieved by subvoxel processing using a novel algorithm as explained in detail in the paper. Permeability was obtained through simulation of one phase flow using Lattice Boltzmann method (LBM). The results show that the simulated permeability is comparable to the analytical approximation. The subvoxel processing has proved an effective method of overcoming the partial volume effect for the low resolution micro-CT images.

Summary Coal has long been exploited as a combustive source of energy, but coal bed methane is a recent development and has been attracting global interest only in the couple past decades. Developing of coal bed methane reservoirs demands better understanding of the fluid flow behaviour in coal reservoirs. Coal seam is a naturally fractured reservoir and contains a high amount of mainly localised organic matter. This results in dual-pore system where pores in organic matter are often too small to be efficient flow paths, whereas much larger fractures (known as cleats) are believed to be main conducts from which gas in organic matters can flow out. Intermediate rank coal samples were collected from Southern Qinshui Basin (China). The coal fractures were scanned using micro computed tomography (micro-CT), while scan electron microscopy (SEM) was implemented to scan the coal matrix. Matrix data was used to reconstruct the 3D nano-scale pore structure using the Markov Chain Model. The permeability of the segmented cleat system and nano-pore system were then simulated using the Lattice-Boltzmann method. The simulation results for fracture/cleat were validated by comparison with analytical solution for Poiseulle flow in a single crack, and pore permeability simulation is under investigation.

This paper presents a method for upscaling permeability of fractured coal using the cubic law to quantify permeability of the fracture system. The version of the cubic law that incorporates the length/tortuosity effect available in the literature was modified by including a connectivity parameter. All parameters of the modified cubic law (fracture aperture, porosity, length, and connectivity) were estimated for a set of coal samples using quantitative methods available in the literature. The geometry of the fracture system within the coal samples was determined from micro computed tomography scans. Parameters of the modified cubic law estimated from the scans were validated by comparison of the resulting permeability to the numerical simulation of single-phase fluid flow in fractures, which was developed at the previous stage of this study. The modified cubic law was then used for upscaling of permeability from the millimeter scale to the centimeter scale. It produced the results that match the literature data for the coal from the same region as well as the experimental data for the studied area.

The traditional approach to coal lithotype analysis is based on a visual characterisation of coal in core, mine or outcrop exposures. As not all wells are fully cored, the petroleum and coal mining industries increasingly use geophysical wireline logs for lithology interpretation.This study demonstrates a method for interpreting coal lithotypes from geophysical wireline logs, and in particular discriminating between bright or banded, and dull coal at similar densities to a decimetre level. The study explores the optimum combination of geophysical log suites for training the coal electrofacies interpretation, using neural network conception, and then propagating the results to wells with fewer wireline data. This approach is objective and has a recordable reproducibility and rule set.In addition to conventional gamma ray and density logs, laterolog resistivity, microresistivity and PEF data were used in the study. Array resistivity data from a compact micro imager (CMI tool) were processed into a single microresistivity curve and integrated with the conventional resistivity data in the cluster analysis. Microresistivity data were tested in the analysis to test the hypothesis that the improved vertical resolution of microresistivity curve can enhance the accuracy of the clustering analysis. The addition of PEF log allowed discrimination between low density bright to banded coal electrofacies and low density inertinite-rich dull electrofacies.The results of clustering analysis were validated statistically and the results of the electrofacies results were compared to manually derived coal lithotype logs.

s of the 31 st Annual Meeting of the Society for Organic Petrology: 2014, Volume 31 Sydney, NSW, Australia 27 th September to 3 rd October 2014 ___________________________________________________________________________ 128 ELECTROFACIES ANALYSIS USING HIGH-RESOLUTION WIRELINE GEOPHYSICAL DATA AS A PROXY FOR INERTINITE AND VITRINITE DISTRIBUTION IN LATE PERMIAN COAL SEAMS,

This paper describes a new automatic processing methodology for extracting microresistivity curves from electrical borehole images in unconventional reservoirs. Real wireline geophysical data were used to develop the technique.Resistivity curves are mostly used for reservoir identification when the separation between shallow and deep readings is interpreted as a sign of permeable layer. Being true for the conventional reservoirs, this rule does not work for the unconventional ones, the coal-seam-gas reservoir is among them. The latter is described by more complex relationships between its formation properties and most of them are still to be established.Coal-seam reservoirs are commonly characterized by their density and fracture distribution, which, in turn, affects wireline geophysical-log response. Both of the former mentioned properties are related to the lithotype layering, or brightness, and the thermal-maturity rate and this study investigates the use of microresistivity data for their determination.Borehole electrical images have been chosen as a source of microresistivity data and an algorithm for extraction of these data from microresistivity images has been created.Data from a specific commercial microresistivity imaging tool data were used with this algorithm, which may require some adjustment to be used with other microimagers. It should be noticed that raw data from the microimager were used prior to any static or dynamic processing.

Coal reservoirs are unconventional in the sense that the scale of vertical and lateral variability is high when compared to conventional sandstone reservoirs. This requires higher resolution data (metres to decimetres to sub-microns levels) for calculation of reservoir properties. Rock core is the best, but it’s expensive to obtain and destroyed during analysis. Recent advances in geophysical imaging of the wellbore provide “pseudo-core” from which to characterise the reservoir, but require advanced processing techniques coupled with ground-truthing to make them a reliable alternative. This project sought to develop methods for advanced coal seam gas reservoir characterisation using wellbore geophysical data and image logs, and to use this information to interpret coal seam gas reservoir properties, in particular coal lithotype, in the context of geological development of the Bowen Basin. The first problem solved during the research was a conversion of borehole electrical images into a microresistivity curve. This procedure allowed the researcher to make a step from qualitative analysis of borehole images to the quantitative one. The resulting microresistivity curve was added to the wireline suite for geostatistical cluster analysis and the development of electrofacies. Thus, the resulting wireline suite consisted of gamma ray, bulk density, photo-electric factor, image-based microresistivity and deep laterolog wireline data. Electrofacies analysis produced a coal lithotype profile which was validated by the results of other studies such as mm scale brightness profile logging and maceral content analysis. Implementation of the microresistivity curve method enhanced the resolution of coal profiling to decimetres level, while photo-electric curve allowed distinguishing between vitrinite- and inertinite-rich low density coal. The results of electrofacies analysis were obtained for 26 wells spread over the Northern Bowen Basin area and the distribution of the coal electrofacies were analysed for both stratigraphical and geographical trends. It was observed that the inertinite-rich coal interpreted from electrofacies analysis increases towards the top of Late Permian coal and towards the north of the Northern Bowen Basin area. These results were consistent with previously observed trends defined by petrographic analysis. Overall, this research project has resulted in the development of a new method for coal characterisation which goes beyond simple cut off values for density, and allows coal lithotype advanced characterisation based on high-resolution wireline borehole data. This method was tested in a case study where stratigraphical and geographical distributions of coal lithotypes were generated over the Northern Bowen Basin area.