With mature integrity programs under heightening public scrutiny, the focus of pipeline integrity management is shifting to outlier situations, challenging morphologies and conditions that are not so common but nevertheless remain a challenge for risk mitigation through conventional inspection and integrity assessment. With this change in focus has come a change in expectation of the data collected, inspection capabilities and quality of results. In this paper, the treatment and characterization of complex defect morphologies and integrity conditions, and how advancement of ILI data analysis processes & techniques are specifically targeting these needs is discussed.
It will outline the distinction between the generation of specifications vs validation by real-world sources (such as conventions of POD, POI, POS) and the impact that uncertainty has for integrity assessment at the given defect level as well as pipeline segment level, depending on approach used to define and characterize the defects of interest.
It will outline through examples, the limitations that historic convention (as used to define defect types of inspection specifications) has on the validation process of challenging pipeline anomalies for use in integrity management. Further characterizing and refining integrity objectives and parameters leads to a natural evolution to address new definitions, terminologies, outliers and concerns of pipeline integrity.
Various examples are presented including the successful use of machine learning today and since the 1990’s, the change in perception of tool performance validation on specific morphologies relative to large “Big Data” catalogues of dig data already collected, and furthermore, its use to target these “outliers” issues using real field data to improve anomaly characterization in addition to depth and burst pressure performance.
Within today’s conventions for defining inspection performance, cases are presented where even high-performance specifications are reliably exceeded on a regular basis with beneficial implications to the effectiveness and efficiency of integrity programs.