Modular Pipeline Risk Assessment
Proceedings Publication Date
Pipelines are recognised as one of the safest methods of transporting hazardous products, yet they pose an inherent and constant risk to the local population, infrastructure and environment. It is therefore incumbent upon an operator to demonstrate that they have strategies in place that maintain this risk as a low as reasonably practical. To accomplish this, the operator should complete an appropriate risk assessment to identify any significant threats and then apply sufficient measures to mitigate them. What constitutes an appropriate risk assessment is a matter of much debate across the industry. In any risk assessment there is a balance to be struck between data availability & acquisition costs; model complexity and transparency; practicality and regulatory compliance. This balancing process has resulted in a multitude of different risk assessment methodologies, each having its own strengths and weaknesses. One size most certainly does not fit all applications, for example out-of-the-box software solutions are often inappropriate through their reliance on extensive and complete data sets, opaque decision processes and conservative assumptions that can disguise results or mislead the risk analyst. This paper describes the development of a pipeline risk assessment methodology, incorporating a combination of mathematical logic and statistical elements into a modular framework. A customised risk model is constructed from these modules depending on the pipeline location (onshore / offshore), fluid type, availability of information, data quality and required level of model complexity. Whatever level is chosen, the risk model provides an operator with consistent and quantifiable results that are fully transparent. The methodology is integrated into the Rosen integrity management software suite (ROAIMS) and has been successfully implemented by a number of major oil and gas operators around the globe. Some of these applications will be illustrated in the paper.