Applications of Physically Accurate Deep Learning for Processing Digital Rock Images

Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis. Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques. Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks

Effective permeability of an immiscible fluid in porous media determined from its geometric state

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to underground $CO_2$ storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with $R^2 = 0.98$.Comment: 6 Pages, 2 Figures, and Supporting Materia

Digital Rock Segmentation for Petrophysical Analysis With Reduced User Bias Using Convolutional Neural Networks

Pore‐scale digital images are usually obtained from microcomputed tomography data that has been segmented into void and grain space. Image segmentation is a crucial step in the process of digital rock analysis that can influence pore‐scale characterization studies and/or the numerical simulation of petrophysical properties. This is concerning since all segmentation methods have user‐selected parameters that result in biases. Convolutional neural networks (CNNs) provide a way forward since once trained, CNN can provide consistent and reliable image segmentation with no user‐defined inputs. In this paper, a CNN is used to segment digital sandstone data, and various ground truth data sets are tested. The ground truth images are created based on high‐resolution microcomputed tomography data and corresponding scanning electron microscope data. The results are evaluated in terms of porosity, permeability, and pore size distribution computed from the segmented data. We find that watershed‐based segmentation provides a wide range of possible petrophysical values depending on user‐selected thresholds, whereas CNN provides a smaller variance when trained on scanning electron microscope data. It can be concluded that CNN offers a reliable and consistent way to segment digital sandstone data for petrophysical analyse

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Petrophysical analysis of coal samples from Gloucester Basin, New South Wales, Australia

Coalbed methane (CBM) as a significant unconventional resource has been developed for more than half a century. In order to exploit CBM effectively, it is essential to study coal chemical properties such as ash content, as well as coal petrophysics such as permeability and porosity. Coal ash estimation and permeability analysis comprise this thesis. All coal samples are from Pontilands 03 coring well, Gloucester Basin, New South Wales, Australia. The main works of this thesis are published in two peer-reviewed journals: Coal ash content estimation using fuzzy curves and ensemble neural networks for well log analysis – International Journal of Coal Geology and Coal permeability: Gas slippage linked to permeability rebound – Fuel.Many important variables for reservoir development and production cannot be derived analytically from continuous well logs. Empirical regression and classification techniques have been widely used to predict these variables from well logs. This approach generally uses data from core analysis and well logs to train a model, which can then be used to estimate a variable where core analysis data are not available. In formation evaluation, the amount of training data is limited or costly to acquire, which often results in regression models having limited predictability. This paper addresses the problem of sparse data by using fuzzy logic and ensemble neural networks to estimate coal ash content from a collection of sparse data. Ash content is a significant parameter to evaluate coal quality and it is usually measured from proximate analysis in the laboratory. Ash content is estimated based on the components of six major oxides (Al2O3, SiO2, K2O, CaO, Fe2O3 and TiO2) by using an X-ray fluorescence technique. Fuzzy curve analysis is used to rank the relationships between well log and ash content data to determine input parameters for estimating ash content. The data sets were then sampled with a bootstrap-aggregating algorithm to create a number of training sets for building ensemble neural networks. The neural networks were trained individually and outputs were combined into an ensemble to estimate ash content. In total 20 core samples were collected from a New South Wales (Australia) coal seam in Gloucester Basin and analysed for ash content. Well logging data included: density, photoelectric, Gamma ray, neutron, acoustic, resistivity, spontaneous potential, and resistivity imaging logging techniques. The tested algorithm produces a repeatable ash content prediction (standard deviation of repeated predictions is 0.43%) and effectively reduced the prediction variance and bias compared to single neural network with early stopping algorithm and multivariate analysis. The workflow is data-driven and could be used to estimate other complex variables that are required when evaluating coal seam gas formations.Coal ash content has been closely linked to permeability. However, many other factors/mechanisms influence coal permeability including: effective stress, swelling, shrinkage, deformation mechanics and gas slippage. The second half of the thesis focuses on understanding one of the basic mechanisms that influence coal permeability – gas slippage. Rather than finding correlations between ash content and permeability more fundamental research is addressed to provide a better understanding of coal permeability. After an extended period of gas production, coal can have a rebound phenomenon where permeability increases with increasing effective stress. This rebound could have a significant impact on gas recovery during the late stages of a coal seams life cycle. This paper aims to characterise coal permeability by combining laboratory measurements with a simple gas slippage model that explains the rebound phenomenon. Gas and liquid permeabilities of coals are measured at (1) constant confining pressure and (2) constant effective stress. The length scales relevant to gas flow are estimated using mercury intrusion, a permeability slip model, and the kinetic theory of gases, which allows us to estimate the Knudsen number for gas flow. Results show a linear relationship between slip length and the mean free path of gas for all of the tested mean pore pressures. This result suggests that a first order slip boundary conditions is sufficient to explain the momentum exchange at the gas/solid boundary during flow under normal reservoir conditions. A correlation between Knudsen number and increased permeability is developed, which further demonstrates that slippage cannot be neglected in coals when Knudsen number is greater than 0.1. Overall, a simple model is presented to explain permeability rebound in coal by considering only gas slippage. The mechanism of coal shrinkage is not considered, which could also influence coal permeability. However gas slippage is considered in coal permeability models

ClpP protease modulates bacterial growth, stress response, and bacterial virulence in Brucella abortus

Abstract The process of intracellular proteolysis through ATP-dependent proteases is a biologically conserved phenomenon. The stress responses and bacterial virulence of various pathogenic bacteria are associated with the ATP-dependent Clp protease. In this study, a Brucella abortus 2308 strain, ΔclpP, was constructed to characterize the function of ClpP peptidase. The growth of the ΔclpP mutant strain was significantly impaired in the TSB medium. The results showed that the ΔclpP mutant was sensitive to acidic pH stress, oxidative stress, high temperature, detergents, high osmotic environment, and iron deficient environment. Additionally, the deletion of clpP significantly affected Brucella virulence in macrophage and mouse infection models. Integrated transcriptomic and proteomic analyses of the ΔclpP strain showed that 1965 genes were significantly affected at the mRNA and/or protein levels. The RNA-seq analysis indicated that the ΔclpP strain exhibited distinct gene expression patterns related to energy production and conversion, cell wall/membrane/envelope biogenesis, carbohydrate transport, and metabolism. The iTRAQ analysis revealed that the differentially expressed proteins primarily participated in amino acid transport and metabolism, energy production and conversion, and secondary metabolites biosynthesis, transport and catabolism. This study provided insights into the preliminary molecular mechanism between Clp protease to bacterial growth, stress response, and bacterial virulence in Brucella strains