An Investigation in Personnel Safety in Modular Buildings Subject to Hydrocarbon Fire Events

In the past two decades, Quantitative Risk Analysis (QRA) has become a staple safety analysis in offshore oil and gas industry.  A comprehensive QRA study could be a powerful tool if the results are understood and changes are implemented to improve safety in the engineering design.  Conventional QRA study relies on generic leak frequency and parts count method.  One major limitation of conventional QRA study is that it is focused primarily on the loss of containment of hazardous fluids and what happens when they are released.  To reduce the risk to acceptable level, usually post-release mitigation measures are addressed.  It is a challenge to tackle the effectiveness of release prevention safeguards.  This paper presents an approach to address the dominant risk contributors identified in conventional QRA study.  The aim is to increase confidence and facilitate decision making in the design selection of loss of containment prevention safeguards.

Sensitivity analysis is usually conducted as part of a QRA study to evaluate the effect of input parameters.  However, in most cases, sensitivity analysis focuses on the mitigation measures, such as safety system locations or ratings, personnel distribution or modification to the QRA model itself.  Means of release scenario prevention is rarely investigated to the point that it is beneficial to the operation or to the design project.  This paper proposes that in order to maximize the use of QRA results, decision makers should investigate means of scenario prevention by performing reliability analysis (e.g., fault tree analysis, human reliability analysis) on the top contributing scenarios.  This paper presents some examples of reliability analysis performed on top risk contributors.  This paper also presents suggestions on how the results can potentially be applied for the benefit of the operation or design project.  The outcome from the QRA study in combination with reliability analysis will increase decision makers’ understanding of the top risk contributing scenarios.  This approach can be utilized to identify opportunities for improvement in the prevention safeguards.