This paper describes the essence of establishing a dynamic fugacity model, mainly in the framework of assessing the potential risk posed by oil spills during Arctic shipping. Key to evaluating this risk is the fate and transport processes as they occur in ice-covered waters. A review of relevant weathering and transport processes is therefore presented. The study shows that more understanding is needed of the behavior of oil in ice for assessing the risk posed during Arctic shipping.@en

Loss functions describe the economical consequences of the deviations from the target values. In recent years they have been used in wide range of application including process safety assessment. This paper provides a novel analysis to assess potential loss due to process deviation. The assessed losses help to better estimate process economic risk, which in turn assist in effective process system design and operational decision-making. The analysis is presented in four different development stages: (i) loss functions focusing on simple functions; (ii) loss functions with estimated maximum loss; (iii) loss functions focusing on probability distributions; and (iv) loss functions in which both distributions of variables and their dependencies are considered (i.e., hierarchical Bayesian based loss functions). Details discussion on development stages three and four are presented with case studies. First case study demonstrates application of inverted probability distribution and while second case study provides application of the hierarchical Bayesian loss functions. Advantage and disadvantages of different types of loss functions are also discussed. Finally, future research directions have been proposed.

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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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Improved understanding of ecological risk associated with Arctic shipping would help advance effective oil spill prevention, control, and mitigation strategies. Ecological risk assessment involves analysis of a release (oil), its fate, and dispersion, and the exposure and intake of the contaminant to different receptors. Exposure analysis is a key step of the detailed ecological risk assessment, which involves the evaluation of the concentration and persistence of released pollutants in the media of contact. In the present study, a multimedia fate and transport model is presented, which is developed using a fugacity-based approach. This model considers four media: air, water, sediment, and ice. The output of the model is the concentration of oil (surrogate hydrocarbons-naphthalene) in these four media, which constitutes the potential exposure to receptors. The concentration profiles can subsequently be used to estimate ecological risk thereby providing guidance to policies for Arctic shipping operations, ship design, and ecological response measures.

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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.

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Because the oil and gas industry has an increasing interest in the hydrocarbon exploration and development in the Arctic regions, it becomes important to design exploration and production facilities that suit the cold and harsh operating conditions. In addition to well-established minimum class requirements for hull strengthening, winterization should be considered as a priority measure early in the design spiral for vessels operating in the Arctic environments. The development of winterization strategies is a challenging task, which requires a robust decision support approach. This article proposes a risk-based approach for the selection of winterization technologies and determination of winterization levels or requirements on a case-by-case basis. Temperature data are collected from climatology stations located in the Arctic regions. Loading scenarios are defined by statistical analysis of the temperature data to obtain probabilistic distributions for the loadings. Risk values are calculated under different loading scenarios. Based on the risk values, appropriate winterization strategies can be determined. A case study is used to demonstrate how the proposed approach can be applied to the identification of heating requirements for gangways.@en

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.

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Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.

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Increasing activities in the Arctic and Antarctic waters have drawn awareness of the risks of shipping in these regions. Winterization of vessels needs to be considered at the design stage to mitigate the risk of icing, freezing or other damages due to low temperature. The temperature that should be used for design and equipment procurement specification must be defined. Classification societies have proposed design temperatures such as the design service temperature (DST) and the minimum anticipated temperature (MAT). The DST is selected to be at the lowest mean daily average for the operational window and geographical location. However, there is very limited guidance provided to define the MAT. The lack of analytical tools that can be used to develop design temperatures still remains a problem. This paper proposes a risk-based approach to estimate minimum anticipated temperature as design temperatures for vessels intended for service in cold regions such as the Arctic and Antarctic. Hourly air temperature data from climatology stations in these regions were obtained from the website of the National Climatic Data Center (NCDC) which is affiliated to the National Oceanic and Atmospheric Administration (NOAA). The criteria used to select the stations include the time span of available data for at least 20 years, availability of hourly data, and altitude of the station (close to sea level). The analysis procedure involves statistical analysis, consequence quantifications and risk calculations. Results from a case study show that the proposed methodology can be reasonably applied. As the minimum anticipated temperature was obtained by looking at the smallest risk with corresponding larger return period or the smallest probability of occurrence. It is expected to have the smallest risk when the minimum anticipated is applied. In addition, an extreme low temperature contour map of the Arctic region has been developed which provides a quick and useful way to evaluate the temperature profile for voyage planning and winterization requirements. A plot of the ship's route on the extreme low temperature contour map will provide information on the magnitude and duration of the extreme temperatures encountered by the ship during the journey.@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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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.

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Arctic waters have historically been relatively inaccessible for marine transport. Lately, climate change has made more of this region ice-free in the summer season. This has reduced the difficulty of marine transport in Arctic waters. Further, exploration and development of natural resources is increasing in Arctic regions, as is destinational shipping. The unique risk factors of this region, such as extremely low temperature, ice conditions and drifting icebergs, continue to pose threats to transportation. Potential impacts associated with marine transportation accidents warrant contingency plans that recognize that preventative measures may fail. To plan effectively, a transportation accident risk assessment model for Arctic waters is helpful. There is limited work on the development of such models. A new cause-consequences based risk assessment model is proposed here. The model estimates the probability of a transportation accident and also the related consequences during navigation in Arctic waters. To illustrate the application of the methodology, it is applied to a case of an oil-tanker collision on the Northern Sea Route.@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

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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Because the oil and gas industry has an increasing interest in the hydrocarbon exploration and development in the Arctic regions, it becomes important to design exploration and production facilities that suit the cold and harsh operating conditions. In addition to well-established minimum class requirements for hull strengthening, winterization should be considered as a priority measure early in the design spiral for vessels operating in the Arctic environments. The development of winterization strategies is a challenging task, which requires a robust decision support approach. This article proposes a risk-based approach for the selection of winterization technologies and determination of winterization levels or requirements on a case-by-case basis. Temperature data are collected from climatology stations located in the Arctic regions. Loading scenarios are defined by statistical analysis of the temperature data to obtain probabilistic distributions for the loadings. Risk values are calculated under different loading scenarios. Based on the risk values, appropriate winterization strategies can be determined. A case study is used to demonstrate how the proposed approach can be applied to the identification of heating requirements for gangways.@en

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

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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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