Corrosion remains a critical integrity threat for the transmission of oil and gas, necessitating robust monitoring strategies. Inline inspection (ILI) tools is the primary method for characterizing time-dependent corrosion behavior. Accurate forecasting of defect growth requires one or more ILI datasets of corrosion depths along with their uncertainties and measurement times. In the previous work we developed a probabilistic growth model under an assumption that individual depth errors are normally distributed. The model provides direct predictions of most probable depths at future times without relying on growth. More generally, it outputs probability distributions of both depth and growth based on historical ILI depths and their tolerances. We used an extensive dataset of ILIs measured over multiple pipelines throughout their history to showcase the application of the probabilistic model in an integrity management program. We show how an operator can make better informed decisions by considering future depth probabilities for individual anomalies while having a complete view of corrosion depth and growth over the whole pipeline. A user can select a subset of anomalies that pose the highest probability of failure according to individual or general thresholds based on depth values at a specified probability of exceedance set by an engineer.
