As an important consideration in the chemical industry, chemical process safety has received notable attention in China. However, catastrophic chemical accidents still occur. To better understand why accidents continue to occur, this paper presented a diagnostic analysis of 14 major chemical accidents in China from 2012 to 2022 based on VOSviewer software. The authors analysed the correlation between the accident causation and their relationship with the safety management elements. The study observed that inferior process safety culture, intentional violation (rule-breaking) of procedure, inadequate safety training, and illegal operations were the most frequent causes of accidents. These causes highlighted the prominent gaps in PSM in China in the process safety culture, compliance with standards, the conduct of operations, process safety competency, and training & performance assurance. The results based on co-occurrence analysis indicated a strong correlation between these gaps in PSM. Enterprises should pay attention to collaborative management among them. These deficiencies in the enterprise's PSM system showed that the essential defects in China's chemical industry are a poor safety culture, inadequate accident investigation, inadequate training, and a lack of chemical safety personnel. The study recommended that the chemical industry establish superior process safety culture and competency for all personnel, monitor leading and lagging process safety indicators, apply inherent safety, and practice advanced safety management concepts. We hope that the findings can provide China's perspectives and strengths for global chemical safety.@en

Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to gas leakage detection has been rarely explored. In the present work, convolutional network (to model spatial likelihood of leak) is combined with bi-directional long short-term memory layer network, or BiLSTM (to model temporal dependence of leak likelihood) to perform leak detection and diagnosis. The developed model was trained and tested using sequence of concentration profiles generated using open-source simulated data. The model learned successfully to predict gas leakage and classify its size. The study also explores the flexibility of this network to perform quick detection and diagnose with the limited data. While the networks did not require parameter adjustments to achieve high prediction accuracy, further optimization is possible through data selection and pre-processing. The model needs to be further tested for wide range of leak scenarios. At its present condition, the combined application of convolutional network and BiLSTM shows promising results for early and accurate leak detection in natural gas facilities. Experimental results are needed to confirm the effectiveness of the model and data uncertainty.@en

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

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Conventional risk assessment of chemical plants considers process accident related causal factors. In the current geopolitical situation, chemical plants have become the target of terrorism attacks, making security concerns as important as safety. To protect the public and environment from undue risks, security related causal factors need to be considered as part of the risk analysis of chemical plants. This paper presents an integrated approach to dynamically assess the occurrence probability of abnormal events. The abnormal event is a state of a process plant arrived either due to a process accident or an intentional (terrorist) threat. This approach considers both safety and security related risk factors in a unified framework. A Bayesian network is used to model specific evolution scenarios of process accidents directly initiated from security related factors and the interaction of causal factors. This model enables to dynamically analyze the occurrence probabilities of abnormal events and causal factors given evidence; it could also capture the impacts of interaction among safety and security related causal factors on these occurrence probabilities. The proposed approach is applied to an oil storage tank to demonstrate its applicability and effectiveness. It is observed that the effect of dependency between correlative accidental and security related factors significantly change the occurrence probability of abnormal events in dynamical assessment.@en

In this paper, a conceptual framework is developed to improve NaTech safety in the chemical industry. The concept is called EPIC, indicating that emphasis should be put on Education, learning and training, Proactive risk minimization and safety innovation, Intensified informed inspection and analysis, and Cooperation and transparency. Concrete initiatives addressing NaTech for every domain of the EPIC conceptual framework are given. The innovativeness of the EPIC framework resides in the potential of the simultaneous application of initiatives within the four areas of improvement (E, P, I, and C) to make chemical clusters much more resilient with respect to nature-related disasters. As such, the proposed EPIC framework may lead to a much-needed revolution of NaTech safety in the chemical industry.

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Using the emissions produced during the entire life-cycle of a fuel or a product, Life-cycle assessment (LCA) is an effective technique widely used to estimate environmental impacts. However, most of the conventional LCA methods consider the impacts of voluntary releases such as discharged toxic substances and overlook involuntary risks such as risk of accidents associated with exploration, production, storage, process and transportation. Involuntary risk of hazardous materials such as fuels could be quite significant and if ignored may result in inaccurate LCA. The present study aims to develop a methodology for accident risk-based life cycle assessment (ARBLCA) of fossil fuels by considering both the voluntary and involuntary risks. The application of the developed methodology is demonstrated for liquefied natural gas (LNG) and heavy fuel oil (HFO) as fuels of a hypothetical power plant. Adopting a Bayesian network approach, the comparative analysis of the fuels helps an analyst not only overcome data uncertainty but also to identify holistically greener and safer fuel options.@en

This paper reviews the literature that identifies Naturally Occurring Radioactive Materials (NORM) in oil and gas production. It further explains how processes associated with the recovery of oil and gas enhances NORM'S concentration and also develops Technologically Enhanced Naturally Occurring Radioactive Materials (TENORM). It redefines TENORM from technical and scientific perspectives and explains how spectral gamma ray logging technology helps to prove the presence of NORM as an indication of oil and gas presence. This article provides a better understanding of TENORM waste disposal practices that poses serious health and environmental risks. It makes a strong argument for the importance of TENORM risk assessment and management through process safety approaches. Finally, it identifies the knowledge and technical gaps related to TENORM in oil and gas production, which require further studies and research.

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Process accidents in the early 1980s have drawn process safety into the main stream. In the 1990s, risk-based approaches were developed to bring safety into design as well as economic considerations. In the 2000s, the inherent safety approach began to be practiced on a limited basis, and process safety evolution made significant progress. As process systems continue to become more complex and operations move into remote and harsh environments, traditional risk analysis may no longer be sufficient. Dynamic risk assessment is the basis for the next generation of risk and management approaches that help to enable safer complex process systems operating in extreme environments. This article investigates the main contributions in the area of dynamic risk assessment. Then, an overall framework for dynamic risk management of process facilities is proposed.

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Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile. This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.@en

Development of natural resources in harsh environments presents significant technical and logistical challenges. An industrial workshop on "safety and integrity management of operations in harsh environments" was organized by the safety and risk engineering group at Memorial University of Newfoundland to bring together industrial practitioners, regulatory authorities, and research and development institutions to identify the safety and integrity challenges in harsh environments, share experience, and develop a roadmap for desired solutions. This article summarizes the lessons learned from the workshop on safety issues in harsh environments. The workshop identified that there are safety challenges regarding construction and operation including a lack of detailed standards, optimization with respect to winterization, and data scarcity. The remoteness of operations in harsh environments is an additional challenge. Finally, human factors add another set of challenges that arise from the physical and psychological behavior of personnel in harsh and remote environments.

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Over the past eight decades the ice-infested Northern Sea Route (NSR) has been steadily developed although historically it has been of little interest to commercial shipping companies. However, the shorter routes, more open waters, and the increasing demand for shipping have caused shipping companies to reconsider shipping along these routes. The NSR is now part of an overall world transportation system. However, a key problem of shipping in Arctic environments is the extreme low temperatures encountered and sea ice during most of the year. The American Bureau of Shipping (ABS) has provided some guidance for shipping along the Arctic sea routes with winterization of the Arctic going vessel as a suggested requirement. The technology and level of winterization is determined by the design service temperature (DST) estimated by the lowest mean daily average temperature (MDAT) over at least a 20-year period for the intended geographical area and season of operation. Systems that are more susceptible to the lower temperatures have another temperature which ABS designates as the Minimum Anticipated Temperature (MAT). However, the suitability of that value with respect to a risk based assessment on a vessel and its systems is questionable. For a modern risk-based approach to winterization, knowledge of the magnitude and frequency of occurrence for a given duration are prerequisites. This paper presents a new and more rational approach to estimate the DST or MAT. The approach is based on the set of Temperature-Duration-Frequency (TDF) curves developed for a given climate station. For this study, the Arctic was divided into four regions. Annual extreme low temperatures of durations 1, 3, 6, 12, 24, 36, 48, 72, 96, 120, 144, and 168 hours were extracted from the last 20 years of historical record from three to four representative stations in each region. Magnitudes of low temperatures were then estimated from the data for return periods ranging from 2 to 100 years. These estimates are then used to construct the TDF curves that provide estimates of the magnitude of extreme low temperatures for a given return period, for various durations of practical interest. It is proposed that the 100-year return period 1-hour duration lowest extreme temperature be used as the estimate of the MAT. The proposed approach addresses the drawbacks of the current approach and provides a practical estimate of the DST or MAT for risk-based winterization decisions.@en

The increasing demand for energy and the high probability of finding vast reserves have shifted offshore exploration and production activity into colder and harsher environments. Offshore activity increases risk of oil spills in these colder and harsher marine environments. The development of a spill contingency plan requires the prediction of fate and transport of oil. Oil spill trajectory and fate modeling in cold marine environments is an exceedingly complex problem, in which variability of physical environment and oil-ice interactions must be addressed. This paper explores the usefulness of the fugacity approach for spill fate and transport modeling in ice-infested waters through a simulation model that combines surface oil weathering algorithms with Level IV fugacity models. Four bulk compartments are used for modeling: air, ice cover, water and sediment. Weathering of surface oil on and under the ice cover is represented by a system of differential equations. Unsteady state mass balance equations are also developed for each of the four bulk phases. The outputs of the multimedia fate model are time-dependent profiles of oil slick area, fraction evaporated, water content in oil, viscosity, and concentration of oil in air, ice cover, water, and sediment. The application of the proposed model is illustrated through the simulation of a hypothetical oil spill in the Labrador Sea. The proposed model is simple but has the promise that it can be further developed to become directly useful to the simulation of spill behavior in ice-infested waters.@en

Vast reserves in the Arctic and sub-Arctic regions have attracted interest of the oil and gas industry. However, oil and gas development in harsh environments faces significant technical and logistical challenges. A workshop on "safety and integrity management of operations in harsh environments" was organized by the Safety and Risk Engineering Group at Memorial University of Newfoundland focusing on main aspects of asset integrity. The event featured representatives from industry, regulatory authorities, and research and development institutions. Participants shared experience and lessons learned, and together developed a roadmap for achieving desired solutions. This paper briefly reviews the lessons learned from the two-day workshop and shares recent developments and applications of risk-based approaches to degradation modeling, integrity assessment, and inspection and maintenance decision-making in harsh environments. The recently developed novel approach of risk-based winterization method is introduced. This approach helps to analyze how much winterization is sufficient to address local and regional weather loading considering operating envelop and criticality of the components or the system. A case study from the Arctic region is used for discussion.@en

Due to a scarcity of data, the estimate of the frequency of a rare event is a consistently challenging problem in probabilistic risk assessment (PRA). However, the use of precursor data has been shown to help in obtaining more accurate estimates. Moreover, the use of hyper-priors to represent prior parameters in the hierarchical Bayesian approach (HBA) generates more consistent results in comparison to the conventional Bayesian method. This study proposes a framework that uses a precursor-based HBA for rare event frequency estimation. The proposed method is demonstrated using the recent BP Deepwater Horizon accident in the Gulf of Mexico. The conventional Bayesian method is also applied to the same case study. The results show that the proposed approach is more effective with regards to the following perspectives: (a) using the HBA in the proposed framework provides an opportunity to take full advantage of the sparse data available and add information from indirect but relevant data; (b) the HBA is more sensitive to changes in precursor data than the conventional Bayesian method; and (c) using hyper-priors to represent prior parameters, the HBA is able to model the variability that can exist among different sources of data.

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Environmental decision-making in offshore oil and gas (OOG) operations can be extremely complex due to conflicting objectives or criteria, availability of vague and uncertain information, and interdependency among multiple decision-makers. Most existing studies ignore conflicting preferences and strategic interactions among decision-makers. This paper presents a game theoretical approach to solve multi-criteria conflict resolution problem under constrained and uncertain environments. Uncertainties in the quantification of imprecise data are expressed using rough numbers. A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e.; operators, regulators and service engineers) are involved. This game is solved using the generalized maximin solution concept. With the solution (i.e.; optimal weights of the criteria), the rough numbers can be aggregated to an expected payoff for each alternative. Finally, the weights of upper and lower limits of a rough number are employed to transform the expected payoff into a crisp score, based on which all alternatives are ranked to identify the best one. A numerical example is outlined to demonstrate the application of the proposed method to the selection of management scenarios of drilling wastes.@en

Development of natural resources in harsh environments presents significant technical and logistical challenges. An industrial workshop on "safety and integrity management of operations in harsh environments" was organized by the safety and risk engineering group at Memorial University to bring together industrial practitioners, regulatory authorities, and research and development institutions to identify the safety and integrity challenges in harsh environments, share experience and develop a roadmap for desired solutions. This article summarizes the lessons learned from the workshop on safety issues in harsh environments. The workshop identified that there are safety challenges regarding construction and operation including a lack of detailed standards, optimization with respect to winterization, and data scarcity. The remoteness of operations in harsh environments is an additional challenge. Finally, the human factors add another set of challenges that arise from the physical and psychological behavior of plant personnel in harsh and remote environments.

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Rapid development of offshore oil and gas (OOG) production, increasing environmental awareness, and strict legislations support the need for pollution prevention (P2) in OOG operations. However, the implementation of P2 has been impeded due to (1) the lack of well-developed P2 technologies, and (2) the requirement of more re-development, training, and disruption in current operations compared to pollution control technologies. In this regard, this paper investigates the P2 opportunities in OOG operations. Future work is required for the development of reliable and cost effective technologies or practices with respect to these P2 opportunities. Moreover, the integration of P2 and environmental management system (EMS) is proposed for the effective and systematic implementation of P2 options.@en

To implement an environmental management system (EMS) in offshore oil and gas (OOG) operations, decision makers always encounter a problem of how to prioritize the environmental issues for establishing an environmental policy. Analytic hierarchy process (AHP) is a popular method to perform multi-attribute decision-making to solve this problem. In order to deal with vague information, various fuzzy AHP methods have been proposed. However, these methods suffer four serious limitations: (1) there is a tremendous computational requirement; (2) sometimes only triangular fuzzy numbers can be used; (3) adding or deleting criteria/attributes is not easy to operate in the algorithm; (4) inconsistent judgments is more likely to be expected with fuzzy numbers. This paper proposes a hybrid approach using fuzzy inference system (FIS) and fuzzy AHP which not only eliminates the above limitations but also serves as a robust tool for the prioritization of environmental issues in OOG operations. In this approach, a five-level hierarchy is developed. The highest level of the hierarchy corresponds to the goal - prioritization of significance of environmental issues, and the lowest level corresponds to environmental issues, whereas intermediate levels correspond to major concerns (environmental risks) and sub-parameters of risk. The FIS is applied at the lower levels of the hierarchy to infer the major risk parameters. After this, the scores representing the extent of risk are calculated. Fuzzy AHP is used at the higher levels to synthesize the Significance Scores that will help to prioritize environmental issues. An application of the proposed approach is demonstrated through a numerical example.

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Activities in offshore oil and gas (OOG) that cause environmental impacts can be systematically managed through an environmental management system (EMS). Environmental performance evaluation (EPE) is an essential part of an EMS. However, previous studies on EPE indicate that existing lists of indicators little insight into how indicators are modified to more accurately assess environmental performance. In this paper, a way is proposed to identify and define specific environmental performance indicators on a case-by-case basis, which consists of five steps: (1) describing environmental requirements; (2) determining favourable outcomes corresponding to the requirements; (3) identifying required activities or issues to achieve the outcomes; (4) searching for proper measures of the activities or issues; and (5) generating a list of key indicators. Based on these steps, a quality function deployment (QFD) approach is developed to determine key indicators and evaluate environmental performance. To handle uncertainties in QFD, the decision makers' evaluations are quantified through rough numbers using the concept of rough sets. The outputs of the proposed approach are different environmental performance indices. Using these indices, decision makers can easily determine whether an improved performance has been achieved through an EMS. The proposed approach is transparent and promising for use as a unified tool for EPE. An application of the proposed approach is demonstrated through a numerical example.@en