A recent ‘fingerprint’ smart pigging inspection recorded over 40,000 metal loss (corrosion) features in a 57km 42” diameter, dry gas pipeline supplying a major LNG facility in Indonesia. The pipeline had been in operation for less than 6 months. Assessment of these results by the inspection company identified 10 sections of pipe that required repair according to ASME B31.G, indicating that the pipeline was not ‘fit for purpose’. The pipeline operator immediately cut out these 10 sections to ensure the continued safe operation of the new pipeline. A detailed pipeline corrosion study subsequently identified the features as corrosion that had occurred during transport and storage of the line pipe. In addition, the corrosion was found to be less severe than initially thought and the same work assessed the remaining defects and, calculations using DNV Guideline RP F101, showed that the features were all acceptable. It was concluded that the high sensitivity of the smart pigging tool, combined with the failure to identify the cause of the features and the simple initial feature assessment overestimated the significance of the corrosion defects. This demonstrates the need for good care and inspection of line pipe during transport storage and construction. It also highlights the need to conduct engineering assessments to determine the inspection philosophy and to quantify the ‘workmanship’ level of metal loss features acceptable on a fingerprint run, before the run takes place. Otherwise new pipelines containing ‘custom and practice’ defects could be the subject of lengthy and costly disputes between operator and constructor. This paper proposes a method for assessing baseline survey data that provides an acceptance level for pre-existing defects. This methodology will assist operators in assessing smart pigging data from new pipelines.

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