Strengthening quantitative risk assessments by systematic treatment of uncertain assumptions

Abstract

The results of quantitative risk assessments (QRA) are conditional on the background knowledge on which the assessments are based, including phenomenological understanding, models, data and expert statements used, as well as assumptions made. Risk indices established in the risk assessment, such as individual risk numbers and f–N curves, may have a more or less solid foundation, depending for example on the validity of assumptions made. Poor models, lack of data or simplistic assumptions are examples of potential sources of uncertainty “hidden in the background knowledge” of a risk assessment. These uncertainties need to be reflected in the risk assessment. Recently, a method for treating uncertain assumptions in a QRA was suggested. The method is based on the different settings faced when making assumptions in risk assessments, considering beliefs about assumption deviation, sensitivity of the risk index to changes in the assumption, and the overall strength of knowledge involved. In the present paper we apply, test and adjust the method using a risk assessment of a lifting operation related to the oil and gas industry as a case. We find that an adjusted version of the method provides systematic guidance on how to treat uncertainties in a QRA.