Pipeline integrity assessments are often based on conditions that are assumed constant over long sections of pipeline – perhaps entire pipeline systems. Such assumptions generally lead to conservative but unrealistic corrosion growth based results. As integrity assessment methodologies continue to evolve, so does the ability to account for local conditions. When multiple in-line inspections of a given pipeline segment have been performed, Statistically Active Corrosion (SAC) methods may be used to estimate local corrosion growth rates. By incorporating local growth rates into the analysis, fewer features will require mitigation. It is also possible that the re-inspection frequency can be extended. These cost savings can be recognized by operators that use corrosion growth rates determined using statistical multi-inspection comparisons. This paper presents one SAC method that has been used to reduce the number of excavations and extend the re-inspection interval for several pipeline operators. A case is presented in which the local corrosion growth rates from an SAC analysis will be compared against growth rate values traditionally used in Probability of Exceedance (POE) analyses. Additionally, verification of SAC growth rates by examination of the raw signal data is addressed.
Corrosion identification and growth estimation benefits from a combination of statistical analysis and engineering assessment of raw signal data from the inspections. Statistical methods can be used to quickly analyze an entire pipeline and identify specific areas for raw signal assessment for validation. Additionally, spot signal assessment of some areas where statistical techniques did not find active corrosion provides validation concerning potential false negatives.
In most cases, an automated approach alone is very challenging and prone to misconceptions. In such cases, comparing the actual signals recorded in each inline inspection run can improve the automated approach.
