Integrity engineers struggle with determining active vs passive internal corrosion, specifically on legacy pipelines with a history of internal corrosion. ILI tools typically undercall internal metal loss anomalies, and each ILI tool vendor has different pitting algorithms. This can create issues when attempting to compare run-to-run data. There may be a mix of over- and under-called anomalies throughout the entire population that, when compared on a system level, can create a false narrative 
that the corrosion is not active or severe.

This research considers two methods that address this problem: specific identification of newly replaced pipe and an analysis of the distribution of localized pit-to-pit anomaly growth values. Pipe sections that have been replaced between ILI runs essentially act as large coupons, providing valuable data about the active growth of internal corrosion. The second model uses a localized corrosion growth score based on the mean, standard deviation, and skewness of the distribution of individual pit-to-pit anomaly growth measurements. Constituent anomalies for the growth distributions are accumulated in sections spanning roughly 500 feet, designed to be sensitive to local corrosion conditions. Using this approach will reduce the influence of tool bias and provide operators with a ranking system based on a calculated growth score to understand where they have a high density of active internal corrosion and where severe internal corrosion is occurring.