Pipeline integrity assessment using mass inspection data

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Corrosion is a time-dependent hazard for virtually all pipelines. Pipeline failures due to corrosion can lead to severe consequences for society, the economy and environment. In-line inspections (ILIs) of pipelines are used to size corrosion defects in pipelines and subsequent limit state assessment on the measured defects is performed to identify safety-critical defects. ILIs often lead to mass data, particularly for pipelines that are subject to high density internal corrosion. Risk- and reliability-based pipeline integrity management has gained increasing relevance over the last decades. It relies on probabilistic models to infer the actual corrosion growth from the ILI results that are subject to inspection uncertainties. Current probabilistic corrosion growth models either focus only on a subset of the available inspection data or are not designed to efficiently process mass inspection data. As a consequence, pipeline integrity assessment and decision making can become biased due to required data truncation leading to sub-optimal conclusions. The objective of this research program is to develop efficient probabilistic models for the integrity assessment of corroded energy pipelines based on mass ILI data. Three research tasks are proposed that will result in two corrosion growth models, one for unmatched defects and one for matched defects.***A probabilistic corrosion growth model for unmatched defects is developed in the first task using a population-based approach. Considering all measured defect sizes from an ILI as one population, the population of actual defect sizes is determined per ILI by making adjustments for sizing, detectability and false call uncertainties. The current and future defect sizes are then inferred for the integrity assessment of entire pipeline segments.***The second task focuses on the development of a probabilistic matching algorithms for corrosion defects from mass ILI data. Reported defect locations from two or more ILIs rarely match exactly due to measurement errors, high-density corrosion and growth of new defects. Existing methods such as iterated-closest-point method are adopted from the field of computer visions. They are adjusted for the increased number of false calls and the possibility that several defects from previous inspections grew together in one defect. A probabilistic model is investigated to rank the possible defect combinations for describing the matching uncertainties.***A hierarchical corrosion growth model for matched defects is developed in the third task. In addition to the existing capabilities of sizing error adjustment and a stochastic process to model the actual defect growth, the hierarchical model includes the developed matching uncertainties. It is extended to analyze the defect growth in axial, circumferential, and radial direction and an efficient approach to estimate the unknown random variables is investigated.*********************