PR-244-153719-R01 Quantification of ILI Sizing Uncertainties and Improving Correction Factors

Abstract

Operators routinely perform verification digs to assess whether an inline inspection (ILI) tool meets the performance specified by the ILI vendors. Characterizing the actual ILI tool performance using available field and ILI data is a difficult problem due to uncertainties associated with measurements and geometric classification of features. The focus of this project is to use existing ILI and excavation data to develop better approaches for assessing ILI tool performance. For corrosion features, operators are primarily interested in quantifying magnetic flux leakage (MFL) ILI tool sizing error and its relationship to burst pressure estimates. In previously completed PRCI research, a limited MFL ILI dataset was used to determine the corrosion feature depth sizing bias and random error using principles published in API 1163 (2013). The research demonstrated the tendency for ILI predictions to be slightly lower than field measurements (i.e., under-call) for the dataset studied, and it provided a framework for characterizing this bias. The goal of this project was to expand on previous work by increasing the number and type of feature morphologies available for analysis, and by estimating the sizing error of ILI measured external corrosion features. New geometric classification criteria, complementing the current criteria suggested by the Pipeline Operator Forum (POF 2009), were also investigated. Lastly, correction factors based on burst pressure prediction accuracy were developed to account for the effect of adopting various feature interaction rules. This report has a related webinar (member login required).