The US regulations and Canadian standards require that a System Wide Risk Assessment (SWRA) be performed for all pipelines. Typically, an annual SWRA is performed by operators and used to identify high risk sections. Appropriate identification of these high risk sections is expected to avoid significant failures, particularly in higher consequence locations. With current heightened public awareness levels and related regulatory oversight even a failure, such as a rupture, with relatively low safety and environmental consequences is considered undesirable. Post failure analysis often examines SWRA results to investigate if SWRA is identifying such locations appropriately. Are SWRAs developed with the intention of avoiding these failures? How can we ensure SWRA achieves these expectations?

This paper examines the purpose of SWRA and takes a data driven approach to critically assess its effectiveness. In the 21st century, where vast amounts of data are being generated through inspections, patrolling, monitoring, and management systems, TransCanada’s approach seeks to leverage all the evidence or leading indicators of high risk and imminent failures. However, data and subject matter expert opinions are not perfect and complete. Understanding these limitations and inadequacies, yet optimizing in the face of them, requires an honest representation of reality with considerations to limits of applicability and probable blind spots, together with clear decision-making to achieve a well-defined purpose.

This paper will describe the six-year evolution of a quantitative SWRA approach with a built in continuous improvement cycle. Examples of learning from failures, assessments, and analytical studies and how they were incorporated into the SWRA are demonstrated. Also the development of meaningful risk targets and their applications are explained. The particular details for scenarios where risk criteria have been exceeded in both high consequence and low consequence locations are examined and interpreted such that maintenance teams can address issues appropriately. The value of bringing all relevant data to a common risk platform is also demonstrated. In the 21st century, where data availability will only increase, appropriate holistic incorporation of these multiple data sets is critical to identify where multiple threats interact. Depending on how likelihood of failure and the consequences of failure are combined, the resultant risk could potentially be high (i.e. different risk measures). Therefore, it is important to cover all risk measures that are relevant and develop criteria that govern these risk measures.

The implementation of a holistic SWRA to make the best optimized decisions possible is demonstrated in practical situations where inputs are imperfect and vast data sets need to be combed for meaningful indicators.

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