Quantitative analysis approaches based on structural reliability methods are gaining wider acceptance as a basis for assessing pipeline integrity and these methods are ideally suited to managing metal loss corrosion damage as identified through in-line inspection. The essence of this approach is to combine deterministic failure prediction models with in-line inspection data, the physical and operational characteristics of the pipeline, corrosion growth rate projections, and the uncertainties inherent in this information, to estimate the probability of corrosion failure as a function of time. The probability estimates so obtained provide the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect. While much has been written in recent years on these types of analyses, the authors are not aware of any published methods that address all of the factors that can significantly influence the probability estimates obtained from such an analysis. Of particular importance in this context are the uncertainties associated with the reported defect data, the uncertainties associated with the models used to predict failure from this defect data, and the approach used to discriminate between failure by leak and failure by burst. The correct discrimination of failure mode is important because tolerable failure probabilities should depend on the mode of failure, with lower limits being required for burst failures because the consequences of failure are typically orders of magnitude more severe than for leaks. This paper provides an overview of a probabilistic approach to corrosion defect management that addresses the key sources of uncertainty and discriminates between failure modes. This approach can be used to assess corrosion integrity based on in-line inspection data, schedule defect repairs and provide guidance in establishing re-inspection intervals.

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