Objective
A major energy operator conducted in-line inspections (ILI) on a 30 km carbon steel subsea production flowline. The first ILI was a baseline pre-service and the recent ILI did not report any evidence of internal corrosion, in contrast to expectations.
The operator engaged Imrandd to carry out detailed statistical analysis of the ILI data to identify wall loss associated with internal corrosion, to assess corrosion behaviour over the length of the line and to review and update the corrosion management KPIs.
Scope of work
Imrandd was tasked with quantifying the historical accumulation of corrosion in the line and estimating corrosion rates during the different phases of historical operation. These were compared to the existing corrosion models and discrepancies identified. A detailed analysis of the corrosion management KPIs was also performed to identify critical KPIs for pipeline health and provide recommendations for future KPI usage.
Data analysis
- Data preparation: extraction of wall thickness and caliper data from the raw data files (measurements at increments of less than 10 mm x 10 mm for the full length of the line), with statistical methods to identify spurious measurements and align each spool for comparison
- Assessment of ILI tool inspection performance metrics
- Statistical analysis to identify and quantify corrosion growth, comparing wall thickness distributions and block minima for the two ILI runs
- Compensation for measurement error effects, enabling reliable identification of sub-millimetre changes in wall thickness and corrosion depth
- Wall loss identification: maximum wall loss per spool along the line and analysis of corrosion maps
- Future wall loss estimation based on operating conditions and the ILI findings
Results
The ability of the analysis approach to identify and quantify corrosion at lower levels than the standard ILI reporting criteria provided substantial insights into actual corrosion behaviour. This highlighted differences to the existing corrosion model predictions and provided a strong evidential basis for updates to the corrosion management KPIs.
The results also showed that subsea corrosion monitoring spools provided limited insights, and certain KPIs did not accurately reflect cumulative wall loss, which may have skewed corrosion risk perception.
Value
Ensuring data integrity and accuracy, coupled with detailed statistical analysis, gave the operator much greater confidence in identifying corrosion characteristics and understanding the current condition of the pipeline. Refining KPIs allowed better alignment with actual pipeline conditions and improved corrosion and integrity management. Imrandd confirmed the need for corrosion inhibitor adjustments and recommended new cumulative risk KPIs to inform long-term decisions.
The work provided a basis for optimisation of the approach to corrosion management over the remaining life of the pipeline, safeguarding pipeline integrity.
