The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Risk assessment of fire accidents in chemical and hydrocarbon processing industry

Fire disasters are among the most dangerous accidents in the chemical and hydrocarbon processing industry. Fires have been the source of major accidents such as the Piper Alpha disaster (1976), the BP Texas City disaster (2005), the Buncefield oil depot fire (2005), Puerto Rico’s fire accident (2009), and the Jaipur fire accident (2009). The catastrophic impact of fire accidents necessitates a detailed understanding of the mechanisms of their occurrence and evolution in a complex engineering system. Detailed understanding will help develop fire prevention and control strategies. This thesis aims to provide a detailed understanding of fire risk in the hydrocarbon production and processing industry. In order to realize this objective, the work presented in the thesis includes three parts: i) Developing a procedure to study potential fire accident scenarios in an offshore facility with different ignition source locations. This procedure helps to design safety measures. The effectiveness of safety measures is verified using a computational fluid dynamics (CFD) code. This work emphasizes that an FLNG layout must be considered with the utmost care since it is the most effective measure in limiting a potential LNG release and subsequent dispersion effect, and directly influences the fire dynamics and thus limits the potential damage. ii) An integrated probabilistic model for fire accident analysis considering the time-dependent nature of the fire is developed. The developed model captures the dynamics of fire evolution using three distinct techniques Bayesian networks, Petri Nets, and a CFD model. The Bayesian network captures the logical dependence of fire causation factors. The Petri Net captures the time-dependent evolution of a fire scenario. The CFD model captures the dimension and impact of the fire accident scenario. The results in this work show that a time-dependent probability analysis model is necessary for fire accidents. iii) Whether fire alone can cause a domino effect is demystified in the last work. A solid-flame model is used in a CFD framework to calculate the escalation vector for a domino effect; escalation probability is assessed using a probit model. The results demonstrate that a pool fire alone sometimes may not cause a domino effect in the current industry. It is other factors, such as explosion and hydrocarbon leakage, work together with a pool fire to escalate into a domino event, for example, the results shown in the case study of the Jaipur fire accident

Application of Risk Analysis in the Liquefied Natural Gas (LNG) Sector: An Overview

In recent years, the global demand for liquefied natural gas (LNG) as an energy source is increasing at a very fast rate. In order to meet this demand, a large number of facilities such as platforms, FPSO (floating production, storage and offloading), FSRU (floating storage and regasification unit) and LNG ships and terminals are required for the storage, processing and transportation of LNG. Failure of any of these facilities may expose the market, companies, personnel and the environment to hazards, hence making the application of risk analysis to the LNG sector a very topical issue throughout the world. To assess the risk of accidents associated with LNG facilities and carriers, various risk analysis approaches have been employed to identify the potential hazards, calculate the probability of accidents, as well as assessing the severity of consequences. Nonetheless, literature on classification of the risk analysis models applied to LNG facilities is very limited. Therefore, to reveal the holistic issues and future perspectives on risk analysis of LNG facilities, a systematic review of the current state-of-the-art research on LNG risk analysis is necessary. The aim of this paper is to review and categorize the published literature about the problems associated with risk analysis of LNG facilities, so as to improve the understanding of stakeholders (researchers, regulators, and practitioners). To achieve this aim, scholarly articles on LNG risk analysis are identified, reviewed, and then categorized according to risk assessment methods (qualitative, semi-qualitative or quantitative; deterministic or probabilistic; conventional or dynamic), tools (ETA, FTA, FMEA/FMECA, Bayesian network), output/strategy (RBI, RBM, RBIM, facility siting, etc.), data sources (OREDA handbook, published literature, UK HSE databases, regulatory agencies' reports, industry datasets, and experts’ consultations), applications (LNG carriers and LNG fuelled ships, LNG terminals and stations, LNG offshore floating units, LNG plants), etc. Our study will not only be useful to researchers engaged in these areas but will also assist regulators, policy makers, and operators of LNG facilities to find the risk analysis models that fit their specific requirements

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

HAZOP: Our Primary Guide in the Land of Process Risks: How can we improve it and do more with its results?

PresentationAll risk management starts in determining what can happen. Reliable predictive analysis is key. So, we perform process hazard analysis, which should result in scenario identification and definition. Apart from material/substance properties, thereby, process conditions and possible deviations and mishaps form inputs. Over the years HAZOP has been the most important tool to identify potential process risks by systematically considering deviations in observables, by determining possible causes and consequences, and, if necessary, suggesting improvements. Drawbacks of HAZOP are known; it is effort-intensive while the results are used only once. The exercise must be repeated at several stages of process build-up, and when the process is operational, it must be re-conducted periodically. There have been many past attempts to semi- automate the HazOp procedure to ease the effort of conducting it, but lately new promising developments have been realized enabling also the use of the results for facilitating operational fault diagnosis. This paper will review the directions in which improved automation of HazOp is progressing and how the results, besides for risk analysis and design of preventive and protective measures, also can be used during operations for early warning of upcoming abnormal process situations

Subsea Blowout Preventer (BOP): Design, Reliability, Testing, Deployment, and Operation and Maintenance Challenges

Subsea blowout preventer (BOP) is a safety-related instrumented system that is used in underwater oil drilling to prevent the well to blowout. As oil and gas exploration moves into deeper waters and harsher environments, the setbacks related to reliable functioning of the BOP system and its subsystems remain a major concern for researchers and practitioners. This study aims to systematically review the current state-of-the-art and present a detailed description about some of the recently developed methodologies for through-life management of the BOP system. Challenges associated with the system design, reliability analysis, testing, deployment as well as operability and maintainability are explored, and then the areas requiring further research and development will be identified. A total of 82 documents published since 1980's are critically reviewed and classified according to two proposed frameworks. The first framework categorises the literature based on the depth of water in which the BOP systems operate, with a sub-categorization based on the Macondo disaster. The second framework categorises the literature based on the techniques applied for the reliability analysis of BOP systems, including Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Petri Net (PN), Markov modelling, Bayesian Network (BN), Monte Carlo Simulation (MCS), etc. Our review analysis reveals that the reliability analysis and testing of BOP has received the most attention in the literature, whereas the design, deployment, and operation and maintenance (O&M) of BOPs received the least

Failure modeling and analysis of offshore process components

This thesis investigates the risk of offshore oil and gas processing equipment operating in a harsh environment. It comprises of two major studies which form the core of journal papers submitted for publication. The first study presented a new risk assessment methodology with applications to fire scenarios in compressor and heat exchanger units. A sensitivity analysis was conducted to identify the critical components, their interdependence, and importance in causing failure. In the second study, a risk assessment approach is proposed that demonstrates how process system failure risk could be assessed in the absence of complete data. The approach also highlighted the importance of interdependence of the failure causation factors. The Bayesian Network (BN) is used in the study to capture interdependence of and uncertainty the variables. Noisy-OR and Leaky Noisy-OR logics are used to improve uncertainty-handling capacity and overcome the data requirement. Application of the proposed approach is demonstrated on a subsea pipeline failure scenario. As a first step, a Bowtie (BT) was developed which captures all the possible failure causes of a leak and shows the potential consequences of a leak in the subsea pipeline. The BT was then mapped to a BN for OR, Noisy-OR and Leaky Noisy-OR logics. Failure probabilities of Subsea Pipeline and its Safety Barriers were calculated with Bow-tie and Bayesian Network for different Logics. Finally, importance analysis was performed for 21 basic events using OR, Noisy- OR and Leaky Noisy OR Logics to determine safety critical elements. In Summary, this thesis provides scientifically sound and applied approaches to conduct risk assessment of process components with limited data. Applications of these approaches demonstrated on different case studies. Use of the proposed approaches would help better understanding of failure and hence improving safety of process system

A review of cyber security risk assessment methods for SCADA systems

This paper reviews the state of the art in cyber security risk assessment of Supervisory Control and Data Acquisition (SCADA) systems. We select and in-detail examine twenty-four risk assessment methods developed for or applied in the context of a SCADA system. We describe the essence of the methods and then analyse them in terms of aim; application domain; the stages of risk management addressed; key risk management concepts covered; impact measurement; sources of probabilistic data; evaluation and tool support. Based on the analysis, we suggest an intuitive scheme for the categorisation of cyber security risk assessment methods for SCADA systems. We also outline five research challenges facing the domain and point out the approaches that might be taken

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems

Advanced reliability analysis of complex offshore Energy systems subject to condition based maintenance.

As the demand for energy in our world today continues to increase and conventional reserves become less available, energy companies find themselves moving further offshore and into more remote locations for the promise of higher recoverable reserves. This has been accompanied by increased technical, safety and economic risks as the unpredictable and dynamic conditions provide a challenge for the reliable and safe operation of both oil and gas (O&G) and offshore wind energy assets. Condition-based maintenance (CBM) is growing in popularity and application in offshore energy production, and its integration into the reliability analysis process allows for more accurate representation of system performance. Advanced reliability analysis while taking condition-based maintenance (CBM) into account can be employed by researchers and practitioners to develop a better understanding of complex system behaviour in order to improve reliability allocation as well as operation and maintenance (O&M). The aim of this study is therefore to develop models for reliability analysis which take into account dynamic offshore conditions as well as condition-based maintenance (CBM) for improved reliability and O&M. To achieve this aim, models based on the stochastic petri net (SPN) and dynamic Bayesian network (DBN) techniques are developed to analyse the reliability and optimise the O&M of complex offshore energy assets. These models are built to take into account the non-binary nature, maintenance regime and repairability of most offshore energy systems. The models are then tested using benchmark case studies such as a subsea blowout preventer, a floating offshore wind turbine (FOWT), an offshore wind turbine (OWT) gearbox and an OWT monopile. Results from these analyses reveal that the incorporation of degradation and CBM can indeed be done and significantly influence the reliability analysis and O&M planning of offshore energy assets.Shafiee, Mahmood (Associate)PhD in Energy and Powe