Practical Quantitative Risk Assessment of Geological Carbon Storage Projects

Food safety is important not only for the people’s general health and daily life, economic development, and social stability but also for the government’s and country’s image. Microbiological and chemical contamination in food is a major cause of illnesses. Food-borne disease remains a real and formidable problem in both developed and developing countries, causing great human suffering and significant economic losses. New science-based approaches to food safety provide an effective way for governments to protect consumers against food-borne disease and plan appropriate response measures when necessary. Risk assessment is a structured science-based process to estimate the likelihood and severity of risk with attendant uncertainty. For risk assessment many organizations recognize four major elements: hazard identification; exposure assessment; dose–response assessment or hazard characterization; and risk characterization. Risk assessment can be descriptive or narrative, qualitative, semi-quantitative, or quantitative. Both qualitative and quantitative risk assessments are important in different circumstances. Food safety risk assessments are undertaken in response to identified chemical or microbial risks to human health. Chemical risk assessments focus on the presence of chemicals such as food additives, food contaminants, or residues of veterinary drugs. A microbial risk assessment evaluates the likelihood of adverse human health effects occurring after exposure to a pathogenic microorganism or to the medium in which the organism occurs. Activities within risk assessment focus on estimating the risk that a hazardous event or factor will negatively affect a population or subpopulation. This chapter deals with the food safety risk assessment process. It introduces a range of techniques that can be used to support risk assessment in practice and outlines the essential characteristics of a good risk assessment.

Rapid industrialization and urbanization in the 20th century have led to increasing volumes of carbon dioxide being released into the atmosphere[...]

The influence of stress on gas sorption behavior and sorption-induced swelling in coal is critical for the success of CO2-enhanced coalbed methane recovery (CO2-ECBM) and geological carbon sequestration—a key strategy for mitigating climate change and promoting clean energy transitions. However, this influence remains insufficiently understood, largely due to experimental limitations (e.g., overreliance on powdered coal samples) and conflicting theoretical frameworks in existing studies. To address this gap, this study systematically investigates the effects of two distinct stress constraints—constant confining pressure and constant volume—on CO2 adsorption capacity, adsorption kinetics, and associated swelling deformation of intact anthracite coal cores. An integrated experimental apparatus was custom-designed for this study, combining volumetric sorption measurement with high-resolution strain monitoring via the confining fluid displacement (CFD) method and the confining pressure response (CPR) method. This setup enables the quantification of CO2–coal interactions under precisely controlled stress environments. Key findings reveal that stress conditions exert a regulatory role in shaping CO2–coal behavior: constant confining pressure conditions enhance CO2 adsorption capacity and sustain adsorption kinetics by accommodating matrix swelling, thereby preserving pore accessibility for continuous gas uptake. In contrast, constant volume constraints lead to rapid internal stress buildup, which inhibits further gas adsorption and accelerates the attainment of kinetic saturation. Sorption-induced swelling exhibits clear dependence on both pressure and constraint conditions. Elevated CO2 pressure leads to increased strain, while constant confining pressure facilitates more gradual, sustained expansion. This is particularly evident at higher pressures, where adsorption-induced swelling prevails over mechanical constraints. These results help resolve key discrepancies in the existing literature by clarifying the dual role of stress in modulating both pore accessibility (for gas transport) and mechanical response (for matrix deformation). These insights provide essential guidance for optimizing CO2 injection strategies and improving the long-term performance and sustainability of CO2-ECBM and geological carbon storage projects, ultimately supporting global efforts in carbon emission reduction and sustainable energy resource utilization.

The emission reduction of the main greenhouse gas, CO2, can be achieved via carbon capture, utilization, and storage(CCUS)technology. Geological carbon storage (GCS) projects, especially CO2 storage in deep saline aquifers, are the most promising methodsfor meeting the net zero emission goal. The safety and efficiencyof CO2 saline aquifer storage are primarily controlledby structural and capillary trapping, which are significantly influencedby the interactions between fluid and solid phases in terms of theinterfacial tension (IFT) between the injected CO2 andbrine at the reservoir site. In this study, a model based on the randomforest (RF) model and the Bayesian optimization (BO) algorithm wasdeveloped to estimate the IFT between the pure and impure gas–brinebinary systems for application to CO2 saline aquifer sequestration.Then three heuristic algorithms were applied to validate the accuracyand efficiency of the established model. The results of this studyindicate that among the four mixed models, the Bayesian optimizedrandom forest model fits the experimental data with the smallest root-mean-squareerror (RMSE = 1.7705) and mean absolute percentage error (MAPE = 2.0687%)and a high coefficient of determination (R2 = 0.9729).Then the IFT values predicted via this model were used as an inputparameter to estimate the CO2 sequestration capacity ofsaline aquifers at different depths in the Tarim Basin of Xinjiang,China. The burial depth had a limited influence on the CO2 storage capacity.

Overview and current issues in geological storage of carbon dioxide

The monitoring of the integrity of onshore geological carbon capture and storage projects will require an approach that integrates various methods with different spatial and temporal resolutions. One method proven to be quite effective for site assessment, leakage monitoring, and leakage verification is near-surface gas geochemistry, which includes soil gas concentration and gas flux measurements. Anomalous concentrations or fluxes, relative to the natural background values, can indicate the potential occurrence of a leak. However the natural background can be quite variable, especially for CO2, due to biological production and accumulation in the soil that changes as a function of soil type, land use, geology, temperature, water content, and various other parameters. To better understand how these parameters influence natural, near-surface background values, and to examine the potential of different sampling strategies as a function of the survey goals, this paper reports results from two highly different case studies, one from northern Europe (Voulund, Denmark) and one from southern Europe (Sulcis, Sardinia, Italy). The small Voulund site, with its homogeneous soil, climate, and topography, was surveyed twice (in fall and in spring) within the EU-funded SiteChar project to examine the effects of different land use practices and seasons on baseline values. Forested land was found to have lower CO2 concentrations during both campaigns compared to cultivated and heath land, and higher CH4 values during the spring sampling campaign. Continuous monitoring probes showed much more detail, highlighting seasonal changes in soil gas CO2 concentrations linked primarily to temperature variations. The much larger Sulcis site, studied within an ENEAfunded project on potential CO2-ECBM (Enhanced Coal Bed Methane) deployment, was surveyed at the regional scale and on detailed grids and transects for site assessment purposes. Despite the completely different soil and climate conditions, the statistical distribution of the Sulcis data was similar to that of Voulund. Much higher soil gas CO2 anomalies were found at this site, however, due to the less permeable sediments (i.e., better water retention and greater gas accumulation) and the warmer temperatures. Detailed surveys at this site highlighted various significant anomalies, some of which can be explained by near surface biological processes, whereas others, especially helium anomalies, were more difficult to explain. These results show the utility of baseline surveys, and highlight the need for follow-up studies to clarify any unexplained anomalies before any CO2 storage. © S.E. Beaubien et al.

Caprock integrity is a critical concern in the successful implementation of geologic carbon capture and storage (CCS) projects. Caprock can break mechanically by radial cracking, plastic deformation, inner and outer debonding, and channelling. Understanding these failure mechanisms is crucial for designing robust management strategies in CCS applications. The classical approach is limited, so this study uses three-dimensional digital image correlation (3D-DIC) to study caprock sample deformation and failure for carbon capture and storage (CCS) reservoir to ensure caprock integrity. Caprock samples from CCS reservoirs were put through diametrical compression using a precise 100 kN electro-mechanical load frame that moved at a rate of 0.05mm/min. The samples had a wide range of minerals. During the diametrical compression tests, a 3D-DIC image capture system was set up to watch the samples without touching them at a rate of 5 frames per second. A black-and-white speckle pattern is affixed to the specimen to monitor its deformation underload. The 3D-DIC system is used for image processing, visualization, and analysis of the caprock sample damage process under various load circumstances. The study uses DIC-generated strain maps to analyze the process of caprock samples breaking, revealing a tension-compression ratio between 2% and 5%. The damage evolution process is divided into four stages: initial, linear elastic, elastic-plastic, and plastic damage. The results show that shale damage changes over time when samples have anisotropy and distinct heterogeneity, potentially causing caprock integrity issues. This study focuses on predicting damage processes in caprock integrity in CCS using various methods. 3D-DIC outperforms these methods in terms of test range, affordability, accuracy, and field monitoring. This image-based algorithm is better at understanding anisotropic and heterogeneous conditions. The results will enhance the effectiveness of examining caprock integrity in CCS outperforms other methods in terms of test range, affordability, accuracy, and field monitoring.

Before initiating a project, risks assessment, is a process that its importance has felt in past two decades and has taken a position in project activities. Project's managers apply risk assessment as a preventive method for highly possible risks having an unfavorable influence on project objectives. Risk assessment has implemented in three ways: qualitative, semi-qualitative (semi-qualitative) and quantitative. In this paper, the author reviews quantitative and semi-qualitative risk assessment methods in associated with fuzzy sets theory (FST). Moreover, considering three steps of risk assessment process, namely: definition and measurement of parameters, fuzzy inference and defuzzification, the author classified presented articles into three groups of giving opinion methods, assessment methods and defuzzification methods. It is avoided mentioning articles with same assessment methods in this paper. Although giving opinion methods and defuzzification methods have the potential to be worked on, late risk assessment surveys, demonstrate increasing attempt on developing comprehension and reality adjustment in project risk assessment methodologies.

Public risk perspectives on the geologic storage of carbon dioxide

Summary Monitoring carbon dioxide (CO2) saturation plume movement and pressure buildup is critical for ensuring the environmental safety of geological carbon storage (GCS) projects. High-fidelity numerical simulations provide accurate modeling of CO2 plume dynamics, but they are often computationally intensive. Recent advancements in data-driven models have enabled the rapid prediction of CO2 plume movement. By leveraging available simulation data sets, these models offer a more efficient alternative without compromising accuracy. In this study, we adopt the U-Net enhanced graph convolutional neural network (UGCN) to predict the spatial and temporal evolution of CO2 plume saturation and pressure buildup in saline aquifers. Utilizing the U-Net architecture, which incorporates skip connections, enables UGCN to capture high-level features and fine-grained details concurrently. We have created physics-based numerical simulation models that account for both CO2 injection and post-injection periods. By employing the Latin-hypercube sampling method, we generated a diverse range of reservoir and decision parameters, resulting in a comprehensive simulation database. We trained and tested the UGCN model on two different data sets, including a 2D radial reservoir model (Scenario 1) and a realistic Society of Petroleum Engineers Comparative Solution Project b (SPE 11b, Scenario 2) model to train and validate the UGCN model. The performance of the UGCN model was compared with other standard graph neural networks (GNNs), such as graph convolution network (GCN) and graph attention network (GAT). Notably, the UGCN model demonstrated robust performances on the blind testing dataset, achieving an R2 score of 0.993 and 0.989 for saturation predictions in Scenarios 1 and 2, respectively. Similarly, for pressure buildup the new model achieved an R2 of 0.989 and 0.999 for Scenarios 1 and 2, respectively. These prediction results indicate the effectiveness of the trained models in predicting the temporal and spatial evolution of CO2 saturation and pressure buildup predictions. Moreover, the prediction central processing unit (CPU) time for the deep learning (DL) models is significantly lower (0.02 seconds per case) than the physics-based reservoir simulator (on average, 10–15 minutes per case). This underscores the capability of the proposed method to provide predictions as accurate as physics-based simulations while reducing substantial computational costs.

Patients with rheumatoid arthritis (RA) have an increased risk of developing cardiovascular disease (CVD). Identification of at-risk patients is paramount to initiate preventive care and tailor treatments accordingly. Despite international guidelines recommending all patients with RA undergo CVD risk assessment, rates remain suboptimal. The objectives of this review were to map the strategies used to conduct CVD risk assessments in patients with RA in routine care, determine who delivers CVD risk assessments, and identify what composite measures are used. The Joanna Briggs Institute methodological guidelines were used. A literature search was conducted in electronic and grey literature databases, trial registries, medical clearing houses, and professional rheumatology organisations. Findings were synthesised narratively. A total of 12 studies were included. Strategies reported in this review used various system-based interventions to support delivery of CVD risk assessments in patients with RA, operationalised in different ways, adopting two approaches: (a) multidisciplinary collaboration, and (b) education. Various composite measures were cited in use, with and without adjustment for RA. Results from this review demonstrate that although several strategies to support CVD risk assessments in patients with RA are cited in the literature, there is limited evidence to suggest a standardised model has been applied to routine care. Furthermore, extensive evidence to map how health care professionals conduct CVD risk assessments in practice is lacking. Research needs to be undertaken to establish the extent to which healthcare professionals are CVD risk assessing their patients with RA in routine care.Key Points• A limited number of system-based interventions are in use to support the delivery of CVD risk assessments in patients with RA.• Multidisciplinary team collaboration, and education are used to operationalise interventions to support Health Care Professionals in conducting CVD risk assessments in practice.• The extent to which Health Care Professionals are CVD risk assessing their patients with RA needs to be established.

Large‐scale geological storage of carbon dioxide (CO2) is indispensable for mitigating climate change but faces significant challenges, especially in the accurate quantitative assessment of leakage risks to ensure long‐term security. Given these circumstances, this paper proposes an innovative approach for quantitatively assessing CO2 leakage risk to address the previous limitations of limited accuracy and insufficient data. We construct a fault tree and transform it into a Bayesian network–directed acyclic graph, and then use judgment sets along with fuzzy set theory to obtain prior probabilities of root nodes. The feature, event, and process method was utilized to identify key components and subsequently determine the conditional probability table (CPT) of the leaf node. The subjective experience assessments from experts are defuzzified to obtain the CPTs of intermediate nodes. The obtained basic probability parameters are input into the directed acyclic graph to complete the model construction. After calculating the leakage probability using this model, it is combined with the severity of impacts to conduct a comprehensive risk assessment. Furthermore, critical CO2 risk sources can be determined through posterior probability calculations when intermediate nodes are designated as deterministic risk events. The gradual implementation process of the proposed model is demonstrated via a typical case study. The results indicate an overall CO2 leakage probability of 29%, with probabilities of leakage along faults/fractures, caprock, and well identified as 32%, 28%, and 19%, respectively. The project is categorized as a medium‐low risk level. When leakage is confirmed, tectonic movement, thickness, and delamination at interface connections/the presence of cracks are the critical risk sources, and measures to mitigate key risks are outlined. The identified key risk factors conform to empirical evidence and previous research, validating the accuracy of the model. This study is instrumental in CO2 geological storage risk assessment and scalable development program design. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Since 1990, Europe and the European Commission have invested a huge amount of research in the geological storage of CO2, The aim was initially to create an inventory of potential storage sites and volumes, later to build models to study the subsurface behavior of CO2 and the storage matrix and to forecast possible problems, later still to perform risks analysis at all relevant time scales. Since Statoil started injecting CO2 on an industrial scale off-shore Norway in 1996, the focus has shifted to the monitoring of geological storage. Over the last ten years, experience has been acquired with both large scale projects (Sleipner, Norway; Weyburn, Canada) and more limited in scope, so called “subsurface laboratories” in Germany, The Netherlands and Poland. Two new geological storage projects (In Salah, Algeria and Snohvit, Norway) now provide the opportunity to build on this work. The CO2ReMoVe project started on 1 March 2006. It is being carried out under the umbrella and with the support of the European Commission’s Sixth Framework Programme for Research and Technology Development. For five years, 30 partners from 12 different countries all over the world will invest EUR 15 million in an effort to bring together all relevant research and industry experience and know how in the field of underground storage of carbon dioxide. The consortium of industrial, research and service organizations proposes a range of monitoring techniques, applied over an integrated portfolio of storage sites in order to develop: 1. methods for base-line site evaluation; 2. new tools for monitoring storage and possible well and surface leakage; 3. new tools to predict and model long term storage behavior and risks; 4. a rigorous risk assessment methodology for a variety of sites and time scales; 5. Guidelines for best practice for the industry, policy makers and regulators. The recommendations from these international efforts will form an important step towards a worldwide consensus in licensing and certification of the storage activities in a number of geological media. These will include oil and gas reservoirs, coal seams and aquifers. It can be expected that the project will achieve a better understanding with the policy makers and the public that CO2 can be safely transported and stored in the subsurface and that this storage can at all times be safely monitored. The project will also provide the assurance required for geologically stored CO2 to qualify for credits under the emissions trading mechanism. It can be expected that the project will lead to greater understanding among policy-makers and the public that CO2 can be safely transported and stored in the subsurface, and that this storage can be safely monitored at all times the project will also provide the assurance required for geologically stored CO2 to qualify for credits under the emissions trading mechanism.

SUMMARYIn recent years, mass violence associated with men who identify as involuntary celibates (incels) has been of increasing concern. Incels engage in an online community where misogyny and incitements to violence against women are prevalent, often owing to the belief that women are denying them a ‘right’ to sex. Indeed, inceldom can be considered a form of extremism. Information released about the prepetrators of incel-associated violence consistently suggests that mental disorder is a contributory factor and may increase vulnerability to engaging with the incel community. Depression, autism and personality disorder are particularly relevant. To date, there has been little research into the mental health of incels and how, in some, this contributes to violence. This article considers the associations between mental disorder and inceldom, including the risk factors for incel-related violence, and makes recommendations for best practice in risk assessment and clinical intervention.

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.