Study on microstructure mechanism of sandstone based on complex network theory

Rock contains a large number of micro-pores, which are of different shapes and complex structures. The structure information of sandstones is extracted based on different porosities through X-ray CT (Computer Tomography) scanning, photo processing techniques and complex network method to explore the topological structure of sandstone seepage network. The results show that sandstone seepage network has scale-free property. The minute quantities of pores with more throat connections have vital functions of overall connectivity of sandstone seepage network, while sandstone seepage network has strong robustness with random error. This research can provide reference for across scales research of porous seepage and multi-disciplinary application of complex network theory

Study on microstructure mechanism of sandstone based on complex network theory

Rock contains a large number of micro-pores, which are of different shapes and complex structures. The structure information of sandstones is extracted based on different porosities through X-ray CT (Computer Tomography) scanning, photo processing techniques and complex network method to explore the topological structure of sandstone seepage network. The results show that sandstone seepage network has scale-free property. The minute quantities of pores with more throat connections have vital functions of overall connectivity of sandstone seepage network, while sandstone seepage network has strong robustness with random error. This research can provide reference for across scales research of porous seepage and multi-disciplinary application of complex network theory

High accuracy capillary network representation in digital rock reveals permeability scaling functions

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.Comment: Main article: 11 pages and 4 figures. Supplementary Information: 6 pages and 4 figures. Version 2 includes DOI for microCT datase

Do Pores Exist?—Foundational Issues in Pore Structural Characterisation

This work reviews a range of fundamental theoretical considerations in pore structural characterisation. The pore concept is essential for providing a better understanding of physical processes arising within porous media than purely phenomenological approaches. The notion of a pore structure is found to be independently valid and invariant during theory change concerning said physical processes, even for structural models obtained via indirect methods. While imaging methods provide a more direct characterisation of porous solids, there is often a surfeit of information beyond that which can be wielded with current computing power to predict processes sufficiently accurately. Unfortunately, the pore network model extraction methods cannot decide in advance the level of simplification necessary to obtain the optimum minimal idealisation for a given physical process. Pore network models can be obtained with differing geometrical and topological properties, but similar mass transfer rates, for reasons that are often not clear. In contrast, the ‘pore-sifting’ strategy aims to explicitly identify the key feature of the void space that controls a mass transport process of interest

A morphological visualization analysis of porous structures in phase inversion processes

Porous membranes are an important technology that continually become more established as steps in research lead to its better understanding. Pore formation within the porous membranes is an emerging field, and current research is focused on understanding their development in phase inversion processes. This thesis attempts to aid in the analyis of these types of simulations by providing a visualization tool to extract information about the material and its pores. A morphological analysis was implemented on the structure of three simulated data sets. The analysis consisted of extracting the pore network from the data and creating sets of visualizations to interpret the structure and properties of the material. A special focus with regard to shape of the pores was applied when creating these visual tools. The results showed a good agreement with an intuitive visual analysis of the input data set and the resulting output, and they resulted in new visual tools to better understand the structure of porous membrane development and point to promising work for the future

Integrating Deep Learning into Digital Rock Analysis Workflow

Digital Rock Analysis (DRA) has expanded our knowledge about natural phenomena in various geoscience specialties. DRA as an emerging technology has limitations including (1) the trade-off between the size of spatial domain and resolution, (2) methodological and human-induced errors in segmentation, and (3) the computational costs associated with intensive modeling. Deep learning (DL) methods are utilized to alleviate these limitations. First, two DL frameworks are utilized to probe the performance gains from using Convolutional Neural Networks (CNN) to super-resolve and segment real multi-resolution X-ray images of complex carbonate rocks. The first framework experiments the applications of U-Net and U-ResNet architectures to obtain macropore, solid, and micropore segmented images in an end-to-end scheme. The second framework segregates the super-resolution and segmentation into two networks: EDSR and U-ResNet. Both frameworks show consistent performance indicated by the voxel-wise accuracy metrics, the measured phase morphology, and flow characteristics. The end-to-end frameworks are shown to be superior to using a segregated approach confirming the adequacy of end-to-end learning for performing complex tasks. Second, CNNs accuracy margins in estimating physical properties of porous media 2d X-ray images are investigated. Binary and greyscale sandstone images are used as an input to CNNs architectures to estimate porosity, specific surface area, and average pore size of three sandstone images. The results show encouraging margins of accuracy where the error in estimating these properties can be up to 6% when using binary images and up to 7% when using greyscale images. Third, the suitability of CNNs as regression tools to predict a more challenging property, permeability, is investigated. Two complex CNNs architectures (ResNet and ResNext) are applied to learn the morphology of pore space in 3D porous media images for flow-based characterization. The dataset includes more than 29,000 3d subvolumes of multiple sandstone and carbonates rocks. The findings show promising regression accuracy using binary images. Accuracy gains are observed using conductivity maps as an input to the networks. Permeability inference on unseen samples can be achieved in 120 ms/sample with an average relative error of 18.9%. This thesis demonstrates the significant potential of deep learning in improving DRA capabilities

Multiphysics Modelling of Energy Storage Devices Using Pore Scale Approaches

The advancement in high-resolution X-ray tomography image acquisition techniques has enabled imaged-based modelling of pore-scale transport processes to better understand structural performance relationship in porous media. The porous components in electrochemical energy storage devices such as lithium-ion batteries, fuel cell and redox flow batteries are subject to intense research to maximize performance and hence reduce the cost of energy storage systems. The image-based pore-scale modelling approaches such as direct numerical simulation (DNS) are, however, very computationally expensive and it gets infeasible to simulate a representative element volume of porous structure on a standard workstation or laptop machine. Pore network modelling (PNM) approach has been previously used to simulate large size porous domains of fuel cell and redox flow batteries at substantially lower computational cost, however, its application in lithium-ion batteries has not been attempted due to the multiphysics and transient nature of transport mechanism involved during charging and discharging process. Lithium-ion batteries are considered as the top candidate for electrochemical energy storage, so modelling their structure-performance relationship at less computational cost will enable development of efficient numerical pore network modelling framework. Therefore, this thesis aims towards developing pore network modelling framework for lithium-ion batteries to study the impact of microstructure on multiphysics transport processes occurring inside battery electrodes. The development of lithium-ion battery pore network model requires enhancements in the current implementation of pore network modelling algorithms. For example, current pore network extraction algorithms only extract a single phase from a tomography image (usually the pores). On the other hand, lithium-ion battery electrodes contain three phases, namely active material (e.g. NMC), carbon binder, and electrolyte filled void phase. To resolve this issue, multiphase pore network extraction algorithms were developed that connect any two phases via interconnections. This allowed investigating inter- and intra-phase transport processes between phases which are common in lithium-ion battery. The extraction algorithms were tested on random sphere packings and three-phase lithium-ion battery cathode and found to agree well with experimental data and DNS model. Computational performance of PNM model was also compared with other modelling approaches and found to give appreciable performance gain on large size porous domains, while yielding similar or equivalent results.Although modelling of transport process using the PNM approach is computationally efficient, extracting pore networks from tomography images is a computationally expensive task. Also, image resolution plays a vital role to determine the relative accuracy of extracted geometrical properties and hence simulation accuracy. To remove these bottlenecks, an efficient, parallelized network extraction technique was developed that enabled pore network extraction from massive size images. A geometric domain decomposition technique was adopted to reduce the computational cost of extraction. The network extraction was observed 7 times faster and consumed 50% less RAM when used in parallel and serial mode respectively. Finally, a case study was performed to reduce the effect of resolution during pore network extraction. This enabled more reliable extracted pore networks for pore network modelling studies. Finally, pore network modelling of lithium-ion batteries cathodes was performed to study galvanostatic discharge behaviour of half-cells. A massive reduction in computational cost was observed when compared with DNS approach. The structural features of two electrodes were investigated to understand the performance-structural relationship. Also, particle-to-particle and pore-to-pore analysis was performed to analyze the state of lithiation, solid-phase potential distribution and lithium-ion concentration distribution, electrolyte phase potential distribution in solid and electrolyte phase respectively. The study enabled modelling of large size lithium-ion electrodes to analyze the impact of internal microstructure on the overall performance of the cell. The presented work in this thesis is focused on developing, validating, and applying a pore network modelling framework for lithium-ion battery discharge. It has enabled the study of structural performance relationship of battery electrodes on a particle to particle basis without estimating effective transport properties using empirical or experimental data. The excellent computational performance of PNMs has allowed multiphysics modelling on standard workstation or laptop with minimal computational resources. Although developed for lithium-ion battery cathodes the developed framework can be used for any anode structure or study thermal performance-structure relationship as well