Abstract
Near-neutral stress corrosion cracking (SCC) is an operational integrity problem experienced by pipeline transportation companies since the 1970’s. Pipeline operators have used a number of different methods to predict and locate SCC. Current in-line inspection technology allows for the detection of SCC in pipelines using ultrasonic measurement. However, these tools have size limitations (not available for small diameter pipelines) and can only accurately detect cracks above a certain threshold dimension. To date, predictive models have focused mainly on establishing quantitative relationships between environmental factors and SCC formation and growth. In general, the models used to predict SCC growth have been more successful than the models used to predict the location of SCC formation.
In contrast to previous models that attempted to determine direct relationships between environmental parameters and SCC formation, a model has been developed by statistically analyzing data pertaining to locations along a pipeline where SCC was and was not found during field investigations. The data was analyzed using statistical regression techniques and a multi-variable logistic regression model was created. The model was then applied to a pipeline and verification digs were conducted. The results of the verification digs indicate that the model is able to accurately predict locations with SCC.
