Smart pigs are used as part of an integrity management plan for oil and gas pipelines to detect metal loss defects. The pigs do not measure the defects: they collect signals from on board equipment and these signals are later analysed. Signal analysis is complex; consequently, defect sizing tolerances and confidence levels can be difficult to determine and apply in practice. They have a major effect when assessing the significance of the defect, and when calculating corrosion growth rates from the results of multiple inspections over time. This paper describes how defect sizing tolerances and confidence levels are obtained by pigging companies, and compares standard and high resolution pigs. Probability theory is used by the authors to estimate the likelihood that a defect is smaller or deeper than the reported (by the pig) value for both standard and high resolution tools. The paper also shows how these tolerances can be included in defect failure assessment and the results of multiple pig runs.

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