Inspection and Assessment of Pipelines Affected by Long Axial Corrosion
Proceedings Publication Date
Presenter
Dr. Christoph Jäger
Company
Author
Christoph Jäger, Abdullahi Atto
Part of the proceedings of
Abstract
Many crude oil and water injection pipelines are affected by long axial corrosion. This type of corrosion often extends over substantial pipeline lengths. The shape of the corrosion channel can be complex and range from a very smooth wall thickness reduction to a rugged surface topography whereby the corrosion depth varies significantly over distance. Long axial corrosion anomalies can be reliably detected and sized by means of ultrasonic in-line inspection (UT ILI). Residual dirt and scale poses a challenge for ILI. Thus, effective cleaning inside the corrosion channel is essential. The rugged shape of the corrosion surface and rough internal pipe surface can lead to outliers in the gathered ILI data. Therefore, an elaborated filtering and re-processing of the acquired inspection data is crucial for the integrity assessment. Commonly, long axial corrosion is reported as one metal loss feature per pipe joint with a feature length equal to the length of the pipe joint and a feature depth equal to the maximum depth in the respective pipe. This feature list information usually does not reflect the characteristic properties of the corrosion anomalies. The pressure capacity is thus best calculated using assessment methods like RSTRENG or DNV-RP-F101 Complex Shape that account for the actual depth profile (river-bottom profile, RBP) of metal loss features. Also corrosion growth rates are ideally obtained by comparing RBPs of consecutive inspections. This paper outlines the main results of a recent joint industry project giving guidance on the assessment of long axial corrosion based on the results of ultrasonic ILI. This involves the determination of RBPs, the calculation of the safe operating pressure of the pipeline system, the determination of corrosion growth rates and the extrapolation of the future pressure capacity. The above points are illustrated by real data examples.

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