Topic: Modeling

Note: the following answers are short and direct, sometimes omitting information that might be helpful for full understanding. Please consult your copy of the book or other resources on this site for more complete answers.

So are there are now pipeline RA approaches that are both better and cheaper than past practice?

Yes. RA that follows the Essential Elements* (EE) guidelines avoids the pitfalls that befall many older methods. Yet, we can still apply all of the data that was collected for the previous approaches. Pitfall avoidance, full transparency, and re-use of data makes the approach more efficient than other practices. Plus, the recommended approaches now generate […]

It sounds like you have methods that very accurately predict failure potential. Is this true?

Unfortunately, no. While the new modelling approaches are powerful and the best we’ve ever had, there is still huge uncertainty. We are unable to accurately predict failures on specific pipe segments except in extreme cases. With good underlying data, we can do a decent job of predicting the behaviour of numerous pipe segments over longer […]

What about the concern that a more robust methodology suffers more from lack of any data?

That is a myth. In the absence of recorded information, a robust RA methodology forces SMEs to make careful and informed estimates based on their experience and judgement. From direct estimates of real-world phenomena, reasonable risk estimates emerge, pending the acquisition of better data. Therefore, I would respond that lack of information should drive you […]

Are you advocating exclusively a quantitative or probabilistic RA?

Terminology has been getting in the way of understanding in the field of RA. Terms like quantitative, semi-quantitative, qualitative, probabilistic, etc. mean different things to different people. I do believe that for a true understanding of risk and for the vast majority of regulatory, legal, and technical uses of pipeline RAs, numerical risk estimates in […]

Why do we need more robust results? Why not just use scores?

Even though they were developed to help simplify an analysis, scoring and indexing systems actually add an unnecessary level of complexity and obscurity to a risk assessment. Statistics-centric QRA’s suffer from lack of specificity to the assets being assessed. Numerical estimates of risk – a measure of some consequence over time and space, like ‘failures […]

But if an estimate as to how often a pipeline segment will fail from a certain threat is needed, aren’t numbers needed to ascertain how often similar pipelines have failed in the past from that threat?

No, it’s not essential. It’s helpful to have such numbers, but not necessary and sometimes even counter-productive. Note that the historical numbers are often not very relevant to the future – how often do conditions and reactions to previous incidents remain so static that history can accurately predict the future? Sometimes, perhaps, but caution is […]

If the new risk assessment methods produce results similar to QRA, why not just use classic QRA?

Several reasons, classic QRA is expensive and awkward to apply to a long, linear asset in a constantly changing natural environment – can you imagine developing and maintaining event trees/fault trees along every foot of every pipeline? The classical QRA was created by statisticians and relies heavily on historical failure frequencies. Ask a statistician how […]

How can the new risk assessment methods be both easy and more informative?

More informative, since they produce the same output as the classic QRA but are more accurate. Easy, because they directly capture our understanding of pipelines and what can cause them to fail. The word ‘directly’ is key here – previous methods relied on inferential data and/or scoring schemes that tended to interfere with our understanding.

What are the newest pipeline RA methodologies like?

They are powerful, intuitive, easy to set up, less costly, and vastly more informative than either of the previous approaches. By independent examination of key aspects of risk and the use of verifiable measurement units, the whole landscape of the risks becomes apparent. That leads to much improved decision making.