Process safety performance indicators

For over 50 years to measure safety performance the Lost Time Incident Rate, LTIR was used. Fortunately, over the years the learning attitude towards accidents changed from a retrospective to a pro-active one. In the 90-s the safety management system was introduced. No management though, without the Deming cycle of Plan, Do, Check, Act, and checking, means the need of indicators. Existing LTIR-values were used not realizing these refl ect personal rather than process safety. In 2005 after the BP Texas City refi nery vapor cloud explosion, awareness of the difference broke through and Process Safety Leading and Lagging Metrics were formulated. In January 2012 an international conference was held in Brussels organized by EPSC and CEFIC. Results will be summarized. The paper will explain briefl y, where we are now, and what still is ahead

Proceedings of the Second FAROS Public Workshop, 30th September 2014, Espoo, Finland

FAROS is an EC FP7 funded, three year project to develop an approach to incorporate human factors into Risk-Based Design of ships. The project consortium consists of 12 members including industry, academia and research institutes. The second FAROS Public Workshop was held in Dipoli Congress Centre in Otaniemi, Espoo, Finland, on the 30th of September 2014. The workshop included keynotes from industry, papers on risk models for aspects such as collision and grounding, fire and the human element, descriptions of parametric ship models and the overall approach being adopted in the FAROS project

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation

Blowout fire probability prediction of offshore drilling platform based on system dynamics

A Barrier Failure Model of offshore platform fire is proposed based on the analysis of historical accidents. On this basis, a fault tree is built to analyze the causes of fire accidents and the Fussell Vesely importance method is used to compare the contributions degree of the basic events. However, it is known that the traditional fault tree method has some limitations on predicting the dynamic probabilities of accidents. To improve the situation, a dynamic probability prediction model is proposed by integrating fault tree with a system dynamics model. Firstly, the dynamic probability of blowout fire for offshore drilling is predicted using the proposed model. Secondly, sensitivities of causal factors are analyzed based on mutual information to find out the key contributory factors to blowout fire. The research results provide safety managers with reliable and effective risk control strategies for preventing the occurrence of accidents

A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems