The objective of an effective corrosion management program is to identify and mitigate corrosion anomalies before they reach critical limit states. Often as there are many anomalies on pipelines an optimized program will mitigate the few corrosion anomalies that may grow to a critical size within the next inspection interval, without excavating many of the anomalies that will not grow to a critical size. This optimization of the inspection interval and the selection of anomalies to mitigate depend on understanding of corrosion growth. Prediction of corrosion growth is challenging because growth with time is non linear and highly location specific. These characteristics make simplistic approaches such as using maximum growth rates for all defects impractical. Therefore it is important to understand the salient aspects of corrosion growth so that appropriate decisions on excavation and re-inspection can be made without compromising safety or undertaking undue amounts of mitigative activities.
In the pipeline industry corrosion growth between two in line inspections (ILIs) has been measured by comparing one ILI run to the next. However many types of ILI comparison methodologies have been used in the past. Within the last decade or two comparison techniques have evolved from box matching of defect samples to signal matching of the total defect populations. Multiple comparison analyses have been performed on the TransCanada system to establish corrosion growth rates. Comparison of the results from these various analyses gives insight into the accuracy and uncertainty of each type of estimate.
In an effective integrity management process the best available corrosion growth data should be used. To do so it is important to understand the conservatism and the uncertainty involved in each type of estimate. When using a run-comparison to predict future growth it is assumed that the growth within the last ILI interval will continue (with associated uncertainty) during the next inspection interval. The validity of these assumptions is examined in this study.
In the context of this paper these assumptions are validated for external corrosion on onshore pipelines. Characteristics of internal and offshore corrosion are very different in space and time variation.
Correlations of external corrosion growth in onshore pipelines with defect size and location are also examined. Learning from multiple corrosion growth studies gives insight into the actual corrosion rate variation along a pipeline as well as general growth characteristics. Different types of corrosion growth modeling for use in probabilistic or deterministic integrity management programs are also discussed.