Abstract
Probabilistic Corrosion Growth Rate (PCGR) models are typically studied on one or two Inline Inspections (ILIs) of a small sample size and use evaluation metrics that are operator specific. This comprehensive empirical assessment aims to expand the evaluation of PCGR models by examining anomalies that had distinct Box-to-Box (B2B) matches in four successive ILIs, resulting in 57,678 individual external corrosion anomalies from twelve pipelines of differing products, vintages, coatings, material specifications, and operating environments. This paper evaluates several PCGR models which are a combination of well-known industry models and simulation-based regression techniques. These models were evaluated using a historical train-test split on each studied anomaly set, where three prior ILIs were used to simulate the depth of each anomaly projected to the year of the fourth ILI. The simulated depths were compared to the measured depths of the final ILI. Statistical metrics and graphical techniques were used to measure a model’s predictive ability to effectively simulate the mean depth and uncertainty. The models presented in this paper are starting points for oil and gas transmission pipeline operators with ILI data to model corrosion growth with time. This work provides empirical guidance on how to use multiple corrosion ILI B2B matches to predict corrosion depth growth.
