Making use of external corrosion defect assessment (ECDA) data to predict DCVG %IR drop and coating defect area

Buried pipelines are vulnerable to the threat of corrosion. Hence these pipelines are coated with a protective layer (coating) to isolate the metal substrate from the surrounding environment. With time, the coating will deteriorate which could lead to corrosion. The condition of the coating can be investigated by the external corrosion direct assessment (ECDA) procedure to investigate and monitor corrosion activity on unpiggable pipelines and provides a guideline in maintaining its structural integrity. This paper highlights the results obtained from the ECDA process which was conducted on 250 km of buried pipelines. The results from the indirect and direct assessment part of the ECDA were modeled using the classical quantile regression (QR) and the Bayesian quantile regression (BQR) method to investigate the effect of factors toward the IR drop (%IR) and the coating defect size (TCDA). It was found that the classical method and the Bayesian approach produces similar predictions on the regression coefficients. However, the Bayesian method has the added advantage of the posterior distribution which considers parameter uncertainties and can be incorporated in future ECDAs.