Many statistical investigation techniques, used for decades in numerous applications, will also help us understand pipeline risk. Financial market analyses tools are a good example. The same types of charts, summary statistics, and other analytical tools used there will be appropriate for our data sets.
Posts from October 2015
Does the CoF hazard zone approach make sense for water pipeline risk assessments?
Absolutely. Even though, unlike its hydrocarbon cousins, no thermal scenarios would be expected from water pipe failures, other consequences are numerous and many are related to the distance from the failure location (hazard zones). Modeling is therefore appropriately done using hazard zones, with both direct and indirect consequence considerations, just as we do for hydrocarbon pipelines.
What’s behind the EE guideline document that DNV and you recently released?
We are advocating a degree of standardisation that serves all stakeholders. This list of essential elements sets forth the minimum ingredients for acceptable pipeline risk assessment. Every RA should have these elements. A specific methodology and detailed processes are intentionally not essential elements, so there is room for creativity and customised solutions. DNV’s recognition of […]
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 […]