Uncertain environments demand the use of probabilistic methods to assess pipeline safety, but a realistic and validated probabilistic method to demonstrate post-hydrostatic test (PHT) integrity has eluded the pipeline industry. Traditionally, deterministic methods grow a ‘just-surviving flaw’ (JSF) under worst-case pressure cycling to predict the remaining life of the most severe imperfection which could have survived a pressure test. The limitations of deterministic JSF are self-evident as they routinely produce unreasonable results and even with attempts to include probabilistically modeled material variabilities they have failed to match known historical PHT reliability. A pipeline operator has now developed a novel approach to the task of quantifying marginal pipeline reliability after hydrostatic test. Rather than limiting random variables to only material properties, potential defects are assigned size and pressure cycling values, randomly sampled from validated distributions of defect size and pressure cycling severity (equivalent to location). The number of generated defects is determined by a validated defect density, and defect size remains limited to what could have physically survived the hydrostatic test. The question posed is no longer “what possible sizes of JSF close to discharge survive to time = t under known load conditions”, but rather “what proportion of pipeline segments with similar defect populations would survive to time=t under defined loading conditions?” This represents a fundamental paradigm shift away from considering only a worst-case scenario to quantification of pipeline condition. Monte Carlo simulation time is kept practical by using an equivalent load integral method to compute crack growth.  This proposed methodology has been applied to a selection of pipeline segments with historic fatigue fractures following hydrostatic tests and quantifies the predictability of station reliability over time. Surprisingly, uncalibrated initial results were reasonably predictive of past incidents. Essential input parameters are discussed, including detailed methods for assigning defect size distributions and densities based on extrapolations of field and in-line inspection data, and a minimum defect density floor established based on the PHT fatigue failure of a newly constructed pipeline. While the method originally targets PHT pipeline segments, development of an analogous method managed exclusively by in-line inspection (ILI) tools is under way, the largest potential flaw for ILI-managed assets then dictated by what could have evaded ILI tool detection rather than survived a hydrostatic test. Progress on this development and future suggested research are discussed.


Surviving Population Reliability Projection Methods

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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