|
Abstract:
|
In-line inspection (ILI) tools detect and characterize threats on a pipeline, but are prone to inherent biases (i.e., sizing differences). The results of these ILI surveys are used to assess the criticality of reported anomalies. To effectively manage the threats that may be present, biases between ILI surveys should be identified and taken into account. Bias analyses involve comparing matched anomaly pairs (i.e., metal loss due to corrosion) from the most recent ILI run to an older ILI, comparing ILI runs to excavated field measurements, and comparing unlikely growth areas across the two ILI runs. Pipeline integrity analyses include an Agresti-Coull binomial confidence interval assessing the proportion within specified tolerances. In addition, both a traditional paired t-test, as well as a ratio paired t-test, treat the matched pair data as continuous. Such methods help determine, along with expert opinion, if the ILI data should be adjusted due to a bias. The statistical analysis and associated visual aids are readily accessed in the Power BI CGReal Bias app. This study used representative ILI results to create flexible, interactive, statistical, and visual analyses.
|
