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In a Nutshell:
Typically made from steel, transmission/midstream pipelines are susceptible to both internal and external corrosion. An Integrity Management Plan (IMP) therefore needs to consider the deterioration of such assets and the rate at which deterioration may occur. Pipeline operators often utilize in-line inspection (ILI) to categorize and size corrosion features, while repeat inspections allow corrosion growth rates to be determined. This article looks at how ROSEN Canada worked with one of its customers to identify the most suitable corrosion growth rate (CGA) assessment methodology for one of its pipeline assets.
Initial Findings
In 2019, the ROSEN Group inspected a 57-km, 8-inch pipeline for metal loss corrosion features (among others). This initial in-line inspection (ILI) used axial magnetic flux leakage inspection technology (MFL-A). The inspection detected a number of external corrosion features up to 81% deep that were later confirmed in-field to be up to 88% deep. An overview of what was found within the pipeline can be seen in the chart below. The pipe joints were coated with Yellow Jacket (YJ) and the field joints with heat shrink sleeves. It was apparent that coating damage had occurred and that external corrosion had been active and unmitigated for some time. Most of the shallower corrosion features were located under shrink sleeves; however, the deepest features in the line were located within the pipe body, with clear damage observed to the YJ as shown below.
Upon receiving these results, the pipeline operator took immediate action to mitigate against further corrosion growth by reviewing their cathodic protection program (CP) and excavating and repairing the deeper features in the pipeline. In order to ensure the safe operation of the asset, the operator wanted to be sure these actions were effective in reducing any corrosion growth. Therefore, 15 months later, ROSEN was contracted to re-inspect the pipeline.
Choosing the Way Forward – Together
Before simply conducting a second inspection, it was important to really understand the operator’s goal, and to then determine what options were available and which would best achieve that goal. Had the mitigations been effective? Had corrosion growth been reduced? That is what needed to be answered.
At this point, from an integrity perspective, three corrosion growth determination options were possible:
- A Corrosion Growth Assessment (CGA) with only box matching
- A more refined Corrosion Growth Assessment service (CGApro) that includes box matching with signal matching of select features
- A CGApro with AutoSCAN (signal matching of all reported features)
To compare the three, the box matching option would provide Excel listing only; it effectively matches two feature lists from one inspection to the next. In short, integrity engineers align girth welds, log distances, and orientation (o’clock position) and then match each identified feature, allowing corrosion growth rates in millimeters per year to be calculated based on the calculated depth change and time between inspections. The second option would provide more detail based on a number of manual signal comparisons and pipeline segmentation, yielding a report with optimized recommended growth rates.
However, with both of these approaches, the short (15-month) interval between inspections would result in a significant challenge to confirm whether changes in feature depth were actually the result of genuine corrosion growth, or whether measured changes were a result of the impact of tool sizing accuracy. For example, if an anomaly had been reported with a depth difference of only 12% wall thickness (WT), this could simply be a result of tool sizing accuracies (typically +/- 10% WT on each inspection), or it could be representative of genuine corrosion growth of up to 0.44 mm/year. In addition, with simple box matching, there would have been ~300 unmatched features. This means that based on the box matching results, the pipeline operator would be unable to confidently determine whether new corrosion features had initiated between inspections, or whether they had simply not been reported by the previous ILI due to detection and reporting thresholds.
The third option, CGApro with AutoSCAN (Automated Signal Correlation and Normalization process), would provide signal-to-signal comparison of all reported metal loss indications between two axial field magnetic flux leakage (MFL-A) inspections, using pattern recognition technology. Historic depth changes would then be estimated using the change in signal amplitude and shape. Compared to box matching, corrosion growth rates derived from AutoSCAN are more realistic – and less uncertain. This option could indeed confirm (or exclude) the absence of new corrosion anomaly initiation while also reducing the influence of sizing inaccuracies. The accuracy of this service enables the operator to make confident choices, potentially avoiding costly digs.
Table 1 – Basic differences between the three presented options
After weighing the pros and cons of each option, the operator, together with ROSEN integrity engineers, chose the most advanced service: CGApro with AutoSCAN. The value this option provides far outweighed the others in terms of delivering the data and knowledge that would allow for more confident decisions.
Please note: In this case, CGApro with AutoSCAN was the right choice. There was high corrosion density present, and deep features had been reported. However, in scenarios where this is not the case, CGApro with AutoSCAN might not be appropriate. In short, understanding the situation, the pipeline status and the goal of any integrity work makes it possible to choose the right corrosion growth management strategy for each asset.
Figure 1 – Overview of features in the pipeline
The Right Choice Pays Off
Following inspection and data evaluation, the CGApro with AutoSCAN assessment determined that no features required investigation/repair within five years of the ILI (a typical maximum re-inspection interval). In comparison, a CGA service based on box matching would have resulted in four features requiring investigation. In addition, the results of the CGApro with AutoSCAN provided strong evidence to show that the pipeline operator’s corrosion mitigation attempts had proven to be successful, resulting in the avoidance of unnecessary investment in more excavation, recoats and upgrades to the CP system.
In this particular case, the bottom line was that the confidence CGApro with AutoSCAN offered this operator resulted in a reduction of unnecessary CP investment, excavation and repair costs.
Figure 2 – The deepest features were found in the pipe body showing damage to the Yellow Jacket