Pipeline risk models are used to prioritize integrity assessments and mitigative actions to achieve acceptable levels of risk. Some of these models rely on scores associated with parameters known or thought to contribute to a particular threat. For pipelines without in-line inspection (ILI) or direct assessment data, scores are often estimated by subject matter experts and as a result, are highly subjective. This paper describes a methodology for reducing the subjectivity of risk model scores by quantitatively deriving the scores based on ILI and failure data.
This method is applied to determine pipeline coating and soil interaction scores in an external corrosion likelihood model for uninspected pipelines. Insights are drawn from the new scores as well as from a comparison with scores developed by subject matter experts.