Investigation of past incidents always reveal deficiencies that are not directly equipment-related, but may be non-technical in nature, such as procedural deviation, inadequate communication etc. Past risk assessment models only provide semi-quantitative approaches to incorporate them in the risk assessment and cannot capture their dynamic nature and dependency in a single model. Current research takes up the challenge of developing a framework and step-by-step methodology for quantitatively merging technical, operational, human and organizational factors contributing to the cumulative risk of a barrier failure. It also addresses their dynamic changes with time, considers their interactions with each other and incorporates the uncertainty of parameter estimation to assess the cumulative risk in a facility.
Syeda Zohra Halim, MSc, PhD, in her talk on Cumulative Risk Assessment to Analyze Increased Risk due to Impaired Barriers in Offshore Oil and Gas Facilities, will discuss the methodology developed for extracting statistical data of contributing factors behind barrier failures from past incident investigation reports. The study produced a generic dataset of contributing factors for fire incidents in the US outer continental shelf (OCS). Data showed that failures rates of contributors were non-constant. They are modelled as non-homogenous Poisson process with Power Law and Hierarchical Bayesian Analysis (HBA) is utilized to predict failure within a time period from the generic data. Results show reliability growth for contributors related to ‘design flaw’ and ‘inadequate job safety analysis’ in the US OCS, although a majority of other contributors show deterioration. In the next stage, near-miss data from a particular facility is incorporated to obtain a plant-specific understanding of how and when their next critical failure may occur. Interaction among contributing factors are measured from the analysis of investigation reports. Finally, the cumulative risk assessment model for an offshore unit with safety instruments is developed where the contributing factors are mapped onto a Bayesian Network to provide probability distributions of barrier failure and subsequent incidents. A case study is adopted which assumes 5 near-miss incidents in a facility over a given time and shows how extracted information from the investigation can be utilized to update the generic data to obtain the probability distribution of individual barrier failure so that the existing cumulative risk in the facility can be understood.
Jim Pettigrew, Principal Investigator and Director of Operations of the Ocean Energy Safety Institute, will present an overview of the offshore-focused research at the Mary Kay O’Connor Process Safety Center and the Ocean Energy Safety Institute (OESI).