SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description

Abstract Many process accidents are controlled and mitigated before they escalate to major events. Unfortunately some do go on to produce catastrophic consequences. As the size and complexity of processing facilities increases, the risks posed by accidents are alarming. Operational safety could be improved by giving emphasis to the prevention of incidents, rather than relying on control and mitigative measures. This method is referred to as an “inherently safer design approach”. To prevent major, though infrequent, event occurrence, it is important to consider accident precursors (symptoms of hazards) such as operational deviations, mishaps, and near misses, in order to prevent events at source rather than controlling or mitigating them. This paper describes a recently developed process accident model with predictive capabilities. It uses a combination of event and fault tree concepts to model the cause-consequence relationship. The model relies on process history, accident precursor information, and accident causation modeling. The model is able to capture the process operational behavior, and update the accident likelihood using the Bayesian updating mechanism. The predictive abilities of the model, along with risk estimation, help to develop and prioritize inherently safer design and operational strategies. This is Part I of two papers, focusing on implementation of this methodology. Part II deals with application of the methodology.