An industry-wide database for crack in-line inspection (ILI) data, along with non-destructive examination (NDE) field data, was developed over a series of PRCI projects since 2013. The database can be used to evaluate capabilities of crack ILI technologies, identify potential improvement to ILI tool performance, and guide selection of ILI tools for specific pipelines. As the industry gained experienced in using crack ILI tools, multiple ILI runs were performed for some pipelines, which resulted in multiple depth measurements for the same set of crack features. As part of these projects, two data sets were made available for performing multiple tool run analyses. The first data set included two ILI runs and the corresponding field excavations, and the second data set included three ILI runs and field measurement performed with two different NDE methods.

A Bayesian methodology was applied to make effective use of data from multiple ILI runs and field measurements with NDE tools to increase confidence in crack size estimates. Multiple crack depth measurements were used in two different ways— namely, to improve the characterization of ILI sizing error bias and to update the maximum depth distribution of individual crack features.

For the ILI sizing bias estimate, the tool sizing error bias was defined as the difference between field and ILI measurements. It was shown that using a Bayesian updating approach resulted in a higher confidence in the estimated tool sizing bias in comparison to a simple averaging of the combined data from multiple ILI runs. For the sizing of individual features, Bayesian updating was used to improve estimates of the mean and standard deviation of the depths of individual features. The Bayesian updating procedure increased confidence for the individual feature depths as compared to combining the data into one data set and performing a simple averaging. The approach allows operators to more accurately characterize ILI uncertainties within their crack management program, potentially improving both effectiveness and efficiency.  


A Bayesian Approach for Effective Use of Multiple Measurements of Crack Depths

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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