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Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent’s experience for later decision-making.

The retention of safety-critical egress skills is an essential part of emergency preparedness on offshore petroleum platforms. Virtual environment (VE) training has been shown to be an effective method for teaching basic onboard familiarization and offshore emergency evacuation procedures. This technology has the potential to train crews before they are deployed offshore. This paper investigates the long-term retention and maintenance of emergency egress competence obtained using a virtual offshore platform. In particular, the research aimed to answer two questions: (1) what egress skills can be remembered after a period of 6 months? and (2) how effective is a VE-based retraining program at maintaining egress skills? A two-phased experiment was designed to first teach basic egress skills and subsequently assess skill retention after a 6- to 9-month period. The first phase of the experiment used a simulation-based mastery learning (SBML) pedagogical approach to teach naive subjects the necessary spatial and procedural skills to evacuate safely. In the second phase of the experiment, the same 36 participants were tested after the retention interval on their ability to respond to a series of egress test scenarios. Participants who had trouble remembering the egress procedures were provided retraining on deficient skills. The results of the experiment indicate that emergency egress skills (both spatial and procedural knowledge) are susceptible to skill decay. This paper will highlight the skills that were most susceptible to skill fade after a period of 6 to 9 months and discuss the efficacy of the retraining participants received to return to competence.

The Piper Alpha disaster (1988) witnessed 167 casualties. The offshore safety guidelines developed afterward highlighted the need for effective and regular training to overcome the problems in evacuation procedures. Today, virtual environments are effective training platforms due to high-end audio/visual and interactive capabilities. These virtual environments exploit agents with human-like steering capabilities, but with limited or no capacity to learn routes. This work proposes a sequential route learning methodology for agents that resembles the way people learn routes. The methodology developed here exploits a generalized stochastic Petri-net based route learning model iteratively. The simulated results are compared with the route learning strategies of human participants. The data on human participants were collected by the authors from an earlier study in a virtual environment. The main contribution lies in modeling people’s route learning behavior over the course of successive exposures. It is found that the proposed methodology models human-like sequential route learning if there are no easy detours from the original escape route. Although the model does not accurately capture individual learning strategies for all decision nodes, it can be used as a model of compliant, rule-following training guides for a virtual environment.

Traditional techniques for accident investigation have hindsight biases. Specifically, they isolate the process of the accident event and trace backward from the event to determine the factors leading to the accident. Nonetheless, the importance of the contributing factors towards a successful operation is not considered in conventional accident modeling. The Safety-II approach promotes an examination of successful operations as well as failures. The rationale is that there is an opportunity to learn from successful operations, in addition to failure, and there is an opportunity to further differentiate failure processes from successful operations. The functional resonance analysis method (FRAM) has the capacity to monitor the functionality and performance of a complex socio-technical system. The method can model many possible ways a system could function, then captures the specifics of the functionality of individual operational events in functional signatures. However, the method does not support quantitative analysis of the functional signatures, which may demonstrate similarities as well as differences among each other. This paper proposes a method to detect anomalies in operations using functional signatures. The present work proposes how FRAM data models can be converted to graphs and how such graphs can be used to estimate anomalies in the data. The proposed approach is applied to human performance data obtained from ice-management tasks performed by a cohort of cadets and experienced seafarers in a ship simulator. The results show that functional differences can be captured by the proposed approach even though the differences were undetected by usual statistical measures.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner.•The DynaFRAM is a dynamic FRAM-based tool.•It is able to captures different characteristics of variability.•It facilitates understanding and analysis of variability in complex operations., Graphical abstract Image, graphical abstract

Operators and regulators of offshore and maritime domains should adopt evidence-based safety training to prepare the workforce for emergency egress. This paper uses pedagogical frameworks and data mining tools to identify training gaps in mandatory offshore safety training, and offers evidence-based virtual environment (VE) training solutions. A VE training setting was used as a human behavior laboratory to provide trainees with artificial experience and record their learning progress in the context of evacuating a virtual offshore petroleum platform during a series of credible emergencies. A longitudinal study was conducted to collect data at three critical learning stages: skill acquisition, retention, and transfer. The empirical evidence identified strengths and deficiencies in the VE training. The modeling provided a more comprehensive assessment of the VE training and demonstrated the utility of data-mining tools for future adaptive training applications.

With the advancement of simulation-based training, intelligent agents that can display human-like behavior have become common. From military combat simulations to nuclear power plant simulation, agents have been widely used to facilitate team training (as team mates, opponents, or both). Credibility of these agents is vital to ensure a sound training process. Credibility of the agents largely depends on the credibility of the underlying human behavior representation (HBR) model. This is why validation of the HBR model is necessary to ensure realistic agent behavior. However, the non-deterministic nature of the HBR and the subjectivity in experts’ judgment during the validation process make HBR model validation more challenging compared to physics based models. This paper presents the validation process of an HBR model of general personnel created for use in an offshore emergency training simulator. Three types of agents (naive, in-between, and ideal) are created in the simulator using the HBR model. The paper discusses the use of empirical evidence as referents, along with subject matter experts. A two-level three factor experiment was conducted using 36 participants. Several performance metrics were collected during the experiment, including route selected for evacuation, time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting to the muster station. Data collected during the experimental study have been used in this paper to demonstrate how the use of empirical evidence can facilitate HBR validation. High-level tasks performed during HBR validation are discussed in detail. Special emphasis is given on acceptability criteria testing to ensure that the HBR model performs adequately under different operating conditions. Results show that the proposed HBR model meets the acceptability criteria requirement for all types of agents. In general, the ideal agents exhibited safe behavior during offshore emergency egress, whereas the naive and in-between agents showed erroneous behavior at times. For example, during the simulation runs of a critical emergency scenario where the primary egress route was obstructed by a hazard, the ideal agents either waited and listened to the public address announcement and followed an alternative egress route (60% cases), or they initially chose their preferred route but re-routed immediately after encountering the hazard (40% cases). In all cases, the in-between agents started with their preferred route and re-routed after encountering the hazard, and the naive agents proceeded with their preferred route even when the route was compromised.

The data required to perform Human Reliability Analysis (HRA) for emergency conditions are not readily available and are difficult to retrieve from accident investigations. In the absence of emergency conditions data, the conventional approach of gathering data for HRA is using expert judgment. Expert judgment often suffers from uncertainty, subjectivity, and incompleteness, which makes the reliability of this data collection technique questionable. A more recent approach is to collect data by conducting experiment in virtual environments with human subjects. Though virtual experimental technique addresses the issues of uncertainty, subjectivity, and incompleteness, it still does not consider individual differences while assigning the influence of different factors on human performance. This paper proposes to advance the virtual experimental technique by enabling the consideration of individual differences. An experiment using virtual environment was done to observe performances of 36 individuals during offshore emergency evacuation. By integrating the data collected from the virtual environment into an HRA model, the reliability of each individual was assessed. Sensitivity analysis was then performed to identify the most influential factors that contributed to failure in emergency conditions. This analysis can help identify specific weaknesses that a participant might have. For example, if a participant is found to be more sensitive to a particular factor, then training scenarios with different variations of the factor can be provided to the participant until an accepted level of competency is reached. Identification of a weakness can be combined with adaptive human factor training so that each individual can obtain competence more quickly.

This paper presents the use of a virtual environment for investigating risks associated with human responses in emergency situations on offshore installations. The virtual environment and the risk management can be used to analyze risks associated with human responses by investigating the effectiveness of existing safety measures, identifying areas of improvement, and proposing new designs for safety measures. Problems Dealing with emergency scenarios on offshore installations is a tremendous undertaking. The interaction of personnel using the safety measures in emergency situations can be affected by hazards, environment conditions, malfunctioning equipment, and inadequate emergency preparedness. Such factors have the potential to prevent personnel from arriving at a safe area, increase the level of risks, and consequently, cause injuries or fatalities to personnel. Risks associated with human responses in emergency scenarios are often unforeseen due to difficulties with modeling realistic emergency scenarios. Objective The objective of the research is to study demonstrate the use of virtual environments for investigating and managing risks associated with human responses in emergencies. Method Risk management is employed to assess and manage risks associated with human responses in emergency scenarios. The risk management is tested using experimental data collected from past studies of human responses in virtual environments. Results Risks associated with human responses during emergency scenarios are determined by safety measures, the environment, and the egress route choices that have been taught. Participants’ performance and interaction with improved safety measures are better than the performance of participants without safety measures. Conclusion This paper provides a demonstration of the utility of virtual environments to assess risks associated with human responses in emergency situations. Application The findings of this study may be useful for offshore energy operations.

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.

Objective. This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee's ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual's level of knowledge or skill. Approach. In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into ten blocks) in a novel virtual environment. Time performance, perceived certainty, and EEG signal data were collected. Main results. A significant increase in alpha power and classification accuracy of EEG data from block 1 against blocks 2–10 was observed and stabilized after block 7, while time performance and perceived certainty measures improved and stabilized after block 5 and 6, respectively. Significance. Results suggest that changes in neural activity, which may reflect an increase in cognitive efficiency, could provide additional insight beyond time performance and perceived certainty.

Resilient systems strive to enhance the safety of complex systems through building and developing adaptive technological and organizational capacities. This study aims at analyzing and improving the level of adaptive capacity in a petrochemical plant by means of an integrated quantitative approach. The data were gathered by a questionnaire whose reliability is examined by statistical methods. To compute and analyze the influence of resilience engineering (RE) indicators, teamwork, and redundancy on adaptive capacity, data envelopment analysis (DEA) method was used. The results indicate that teamwork and redundancy have a positive effect on enhancing the level of adaptive capacity. Multilayer perceptron (MLP), a machine learning approach, was used to estimate the level of adaptive capacity on the basis of a dataset. The results of DEA and MLP approaches are confirmed by statistical methods. To the best of our knowledge, this is the first study that measures quantitatively and improves adaptive capacity by an integrated DEA-MLP approach based on the stress-strain model. The outcomes of this study could assist managers and other decision-makers of complex systems to compute and improve the level of adaptive capacity for coping with upcoming events in abnormal conditions.

This data article describes the experimental data used in the research article “Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique” (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants’ performance was tested in 4 different virtual scenarios with different levels of visibility and complexity. Several performance metrics of the participants were recorded during each scenario. The metrics include: time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting at different muster locations.

The main purpose of this study was to model and analyze hospital to home transition processes of frail older adults in order to identify the challenges within this process. A multi-phase, multi-sited and mixed methods design was utilized, in which, Phase 1 included collecting semi-structured interviews and focus group data, and Phase 2 consisted of six patient/caregiver dyad prospective case studies. This study was conducted in three hospitals in three cities in a single province in Canada. The Functional Resonance Analysis Method (FRAM) was employed to model daily operations of the transition process. The perspectives of both healthcare providers and patients/caregivers were used to build the FRAM model. The transition model was then tested using a customized version of the FRAM. The six patient/caregiver cases were used in the process of testing the FRAM model. The results of building the FRAM model showed that five categories of functions contributed to the transition model, including admission, assessment, synthesis, decision-making, and readmission. The outcomes of using the customized version of the FRAM revealed challenges affecting the transition process including waitlists for geriatric units, team-based care, lack of a discharge planner, financial concerns, and follow-up plans. The findings of this study could assist managers and other decision makers to improve the transition processes of frail older adults by addressing these challenges. The FRAM method employed in this study can be applied widely to identify work practices that are more or less successful, so that procedures and practices can be adapted to nudge healthcare processes towards paths that will yield better outcomes.

In the performance influencing factor (PIF) hierarchy, person-based influencing factors reside in the top level along with machine-based, team-based, organization-based and situation/stressor-based factors. Though person-based PIFs like morale, motivation, and attitude (MMA) play an important role in shaping performance, it is nearly impossible to assess such PIFs directly. However, it is possible to measure behavioral indicators (e.g. compliance, use of information) that can provide insight regarding the state of the unobservable person-based PIFs. One common approach to measuring these indicators is to carry out a self-reported questionnaire survey. Significant work has been done to make such questionnaires reliable, but the potential validity problem associated with any questionnaire is that the data are subjective and thus may bear a limited relationship to reality. This paper describes the use of a virtual environment to measure behavioral indicators, which in turn can be used as proxies to assess otherwise unobservable PIFs like MMA. A Bayesian Network (BN) model is first developed to define the relationship between person-based PIFs and measurable behavioral indicators. The paper then shows how these indicators can be measured using evidence collected from a virtual environment of an offshore petroleum installation. A study that focused on emergency evacuation scenarios was done with 36 participants. The participants were first assessed using a multiple choice test. They were then assessed based on their observed performance during simulated offshore emergency evacuation conditions. A comparison of the two assessments demonstrates the potential benefits and challenges of using virtual environments to assess behavioral indicators, and thus the person-based PIFs.

Offshore emergency conditions are dynamic in nature and personnel on board are challenged with high risk, time pressure, uncertainty, and the complexity of the situation. This paper investigates how different attributes of emergency scenarios influence people's choice of egress route subsequent to training. An empirical study was carried out in a virtual environment (VE) with 17 naive participants. The participants were trained to muster during emergencies using a lecture based training (LBT) approach. Training sessions in LBT consisted of computer based training tutorials and simulated training scenarios. Participants’ performance was then tested in simulated testing scenarios. It was observed that given the same training, people used different sets of attributes to make decisions on the egress route. This can help to diagnose causes of poor performance and to design adaptive training lessons. Such identification can also help in the assessment of the efficacy of the training curriculum, or the pedagogical approach. To evaluate the prediction accuracy of the decision trees, the outcomes were compared to the actual observed outcomes of the participants in scenarios in the testing data set. Results show an average of 95% prediction accuracy of the decision trees.

Retention of egress skills is critical during high-stress emergencies on offshore oil and gas platforms. This paper uses a virtual offshore platform to investigate the long-term retention of emergency egress competence. A two-phased empirical experiment was designed to first teach basic egress skills and subsequently assess skill retention. The first phase of the experiment used a simulation based mastery learning (SBML) pedagogical approach to teach 36 naive subjects the necessary spatial and procedural skills to evacuate safely from an offshore platform. In the second phase of the experiment, the same participants were tested after 6 to 9 months on their ability to respond to a series of egress test scenarios. These results indicated that emergency egress skills are susceptible to skill decay. Recommendations to improve the retention of offshore egress skills are discussed.

Bayesian network (BN) is a powerful tool for human reliability analysis (HRA) as it can characterize the dependency among different human performance shaping factors (PSFs) and associated actions. It can also quantify the importance of different PSFs that may cause a human error. Data required to fully quantify BN for HRA in offshore emergency situations are not readily available. For many situations, there is little or no appropriate data. This presents significant challenges to assign the prior and conditional probabilities that are required by the BN approach. To handle the data scarcity problem, this paper presents a data collection methodology using a virtual environment for a simplified BN model of offshore emergency evacuation. A two-level, three-factor experiment is used to collect human performance data under different mustering conditions. Collected data are integrated in the BN model and results are compared with a previous study. The work demonstrates that the BN model can assess the human failure likelihood effectively. Besides, the BN model provides the opportunities to incorporate new evidence and handle complex interactions among PSFs and associated actions.

A bow-tie diagram combines a fault tree and an event tree to represent the risk control parameters on a common platform for mitigating an accident. Quantitative analysis of a bow-tie is still a major challenge since it follows the traditional assumptions of fault and event tree analyses. The assumptions consider the crisp probabilities and “independent” relationships for the input events. The crisp probabilities for the input events are often missing or hard to come by, which introduces data uncertainty. The assumption of “independence” introduces model uncertainty. Elicitation of expert's knowledge for the missing data may provide an alternative; however, such knowledge incorporates uncertainties and may undermine the credibility of risk analysis. This paper attempts to accommodate the expert's knowledge to overcome missing data and incorporate fuzzy set and evidence theory to assess the uncertainties. Further, dependency coefficient-based fuzzy and evidence theory approaches have been developed to address the model uncertainty for bow-tie analysis. In addition, a method of sensitivity analysis is proposed to predict the most contributing input events in the bow-tie analysis. To demonstrate the utility of the approaches in industrial application, a bow-tie diagram of the BP Texas City accident is developed and analyzed.

Event tree analysis (ETA) is an established risk analysis technique to assess likelihood (in a probabilistic context) of an accident. The objective data available to estimate the likelihood is often missing (or sparse), and even if available, is subject to incompleteness (partial ignorance) and imprecision (vagueness). Without addressing incompleteness and imprecision in the available data, ETA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations. A fuzzy-based approach is used for handling imprecision and subjectivity, whereas evidence theory is used for handling inconsistent, incomplete and conflicting data. Application of these approaches in ETA is demonstrated using the example of an LPG release near a processing facility.

HAZOP (Hazard and Operability) is a preliminary and systematic approach for identifying hazards and suggesting hazard mitigation measures in a process facility. A knowledge-based expert system, ExpHAZOP + , has been developed to automate the manual HAZOP analysis and accelerate the process. ExpHAZOP + comprises a graphical user interface (GUI), a knowledge-base and an inference engine. One of the unique features of ExpHAZOP + is the fault propagation algorithm, an aspect of the inference engine, which defines the propagation of deviations to all downstream equipment. The dynamic knowledge-base of ExpHAZOP + allows a user to update knowledge while performing the HAZOP analysis and to use that knowledge in the result. ExpHAZOP + is easy to use and provides results in a standard report format.

Process facilities are well known for unplanned chemical emission, toxic release, fire and explosion and operational disruption. These incidents have the potential to cause an industrial accident and environmental damage. From the investigation of all major accidents, it is apparent that most industrial accidents can be avoided or restricted with a systematic risk analysis and safety management strategy. An effective risk analysis strategy always gives preference to minimizing the risk of a process facility at its design stages. -- Probabilistic risk assessment (PRA) is a comprehensive, structured and logical method for identifying and assessing risks of complex process systems. It uses fault tree analysis (FTA) as a tool to identify basic causes leading to an undesired event, to represent logical dependency of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event. Probability data estimation, and large and complex fault trees, are challenging aspects of FTA as applied to process facilities. -- Quantitative analysis of a fault tree for a process system requires a fault tree and the system components (basic events) failure data. Sometimes or always it is difficult to have an exact estimation of the failure rate of individual components or the probability of occurrence of undesired events due to a lack of sufficient data. Further, due to imprecision in basic failure data or the data sufficiency the overall analysis of a fault tree may be questionable. To avoid such conditions, a fuzzy approach may be used with the FTA technique. This reduces the ambiguity and imprecision arising out of the subjectivity of the data. -- Fault tree construction for a process facility must accommodate for a wide variation in components, process operations and control mechanisms. It is more scientific to analyze such a large and complex fault tree through proper sub-divisions of the tree. A proper modularization technique (sub-division) can sub divide a tree into its equivalent sub trees and then analyze it for the process facility. -- This work is focused on developing a methodology of a fuzzy based computer-aided fault tree analysis tool. The central idea of this methodology is to adopt a suitable algorithm for moduling (sub-dividing) a large and complex fault tree and then evaluate it by using the fuzzy approach. This methodology uses a systematic approach of fault tree development, fault tree modularization, minimal cut sets determination, fuzzy probability analysis, and fuzzy based sensitivity analysis of a system for achieving its objectives. Besides developing a methodology for computer- aided FTA, this study also proposes a procedure of fuzzy approach for the uncertainty analysis, which is used for comparing error robustness of fuzzy FTA and conventional FTA.

Fault tree analysis is a systematic, deductive and probabilistic risk assessment tool which elucidates the causal relations leading to a given undesired event. Quantitative fault tree (failure) analysis requires a fault tree and failure data of basic events. Development of a fault tree and subsequent analysis require a great deal of expertise, which may not be available all the time. Computer-aided fault tree analysis is an easy-to-use approach, which not only provides reliable results but also facilitates the validation and repeatability of the analysis. This enhances the overall results of the fault tree analysis and quantitative risk analysis. This paper presents a revised methodology for computer-aided fault tree analysis. The methodology includes fault tree development, minimal cutsets determination, cutsets optimization and probability analysis. The methodology uses advanced concepts of fault tree development and static and dynamic modularizing for complex and large fault trees. Furthermore, it enables sensitivity analysis of the system for design modification and risk-based decision making. Application of the proposed methodology to a process system is also discussed in the paper.

This paper addresses the severity of consequences of ineffectively performing evacuation operations of offshore installations in harsh environments. Hazards, particularly fire, smoke, heat, and extreme weather conditions, can harm personnel both directly and indirectly and prevent personnel from performing an evacuation operation successfully. To demonstrate the dependence of consequences on the hazards, this paper uses an event tree (ET) analysis. The event tree analysis maps all possible sequences of events leading to an accident and thus, reflects the level of knowledge about the evacuation operation. The paper uses ET analysis to develop a logical approach to hazards and consequences with the presence of safety functions. Applications of an ET analysis are discussed for two emergency response actions: a) detecting the emergency alarm, and b) moving along the escape route. In a situation where the offshore installation manager (OIM) delays activating the emergency alarm or the alarm system is damaged, personnel may not receive or hear the message. Availability of both primary and alternative escape routes enable personnel to move to a safe area in an escalating event, such as a series of fires and explosions. The paper discusses the ET analysis of hazards and consequences in specifically a qualitative manner. Information from the analysis can be used in a risk assessment of evacuation operations.Copyright © 2015 by ASME

As a precursor to simulation transfer studies, this research assessed the level of competence in basic offshore safety gained through a virtual environment training program and investigated the training time required to reach competence. The experiment demonstrated that the offshore egress learning objectives can be taught using the All-hands Virtual Emergency Response Trainer (AVERT) training program with some limitations. The two main findings were: 1. due to individual differences in spatial learning, some individuals required more exposure to the virtual setting to ensure knowledge retention; and 2. the procedural learning objectives required reinforcement during training scenarios to ensure knowledge acquisition. Overall, this research recommends modifications to the training and technology design in order to prepare for future transfer studies and offshore applications.Copyright © 2015 by ASME

Oil and gas accident statistics reveal that workers’ potential for injury or death from occupational accidents is at least as high as that associated with explosions, fires, and other major incidents. The authors’ contribution to the ongoing efforts to improve the situation is a holistic, quantitative model capable of predicting occupational accident frequency. Model inputs include external, corporate and direct factors, and the approach includes methods often favoured by professionals from the safety, engineering, and psychology disciplines. As a precursor to model development, a comprehensive literature review was conducted. One objective of the review was to understand the previous approaches taken by other researchers and thereby identify any gaps in the knowledge. The lack of a holistic, quantitative model addressing occupational accidents in the oil and gas industry was established. A second objective was to select and categorise the factors most influential in the accident process and thereby provide a foundation for the present model. Optimal choices required a systematic study of the factors proposed by other researchers. The literature studied has been summarised in this paper, subdivided into two primary groups: (i) descriptions of existing accident models, and (ii) analyses of existing data. In addition, descriptions of literature specifically concerned with two key elements in the occupational accident process, human factors and safety culture, have been included. Based on the literature reviewed, the novelty of the present model has been discussed. The systematic approach taken to choose the factors thought most influential in the occupational accident process, and which were therefore included in the model, has been described. Some details of the proposed model have been included.

The equations governing the boom-assisted deployment of a lifeboat from a moving support are formulated in three dimensions using Kane's method, accounting for the coupling of the dynamics of the boom and lifeboat. The effect of wind loading is included. At splashdown we assume a controlled water entry and hence the buoyancy force, but not impact force, is modeled. The equations are solved numerically using a standard Runge-Kutta MATLAB routine. Comparison with a known solution for a special case is illustrated.

Previous studies on Ecological Risk Assessment (ERA) of produced water relied on the use of deterministic hydrodynamic models. The assessment was usually carried out in the North Sea context using a model such as the Chemical Hazard Assessment and Risk Management (CHARM), or in the North American context based on the output of a hydrodynamic model such as the Cornell Mixing Zone Expert System (CORMIX). In both these cases, however, probabilistic analysis has not been employed, particularly, to account for uncertainty associated with hydrodynamic models in the ERA study. In fact, it is the hydrodynamic model that has a direct linkage to the selection of the discharge alternatives. Apart from the monitoring purposes, in this article, it is suggested that criteria for evaluating discharge alternatives of produced water in a marine environment might incorporate an awareness of ecological risks by incorporating engineering and toxicological aspects. An ERA methodology consisting of problem formulation...

This paper presents AVERT, a first-person simulator for general offshore workers that exposes them to a virtual environment similar to actual offshore sites and facilitates training in and assessment of spatial awareness, alarm recognition, and emergency response procedures. We follow the technical introduction with a case study showing the efficacy of AVERT by finding a correlation between trainee route selection and hazard exposure in a set of test scenarios.