NDLI: A Practical Application to Calculating Corrosion Growth Rates by Comparing Successive ILI Runs From Different ILI Vendors

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Spencer, Kevin Kariyawasam, Shahani Tetreault, Cathy Wharf, Jon
Copyright Year	2010
Abstract	Corrosion growth rates are an essential input into an Integrity Management Program but they can often be the largest source of uncertainty and error. A relatively simple method to estimate a corrosion growth rate is to compare the size of a corrosion anomaly over time and the most practical way to do this for a whole pipeline system is via the use of In-Line Inspection (ILI). However, the reported depth of the anomaly following an ILI run contains measurement uncertainties, i.e., sizing tolerances that must be accounted for in defining the uncertainty, or error associated with the measured corrosion growth rate. When the same inspection vendor performs the inspections then proven methods exist that enable this growth error to be significantly reduced but these methods include the use of raw inspection data and, specialist software and analysis. Guidelines presently exist to estimate corrosion growth rates using inspection data from different ILI vendors. Although well documented, they are often only applicable to “simple” cases, pipelines containing isolated corrosion features with low feature density counts. As the feature density or the corrosion complexity increases then different reporting specifications, interaction rules, analysis procedures, sizing models, etc can become difficult to account for, ultimately leading to incorrect estimations or larger uncertainties regarding the growth error. This paper will address these issues through the experiences of a North American pipeline operator. Accurately quantifying the reliability of pipeline assets over time requires accurate corrosion growth rates and the case study will demonstrate how the growth error was significantly reduced over existing methodologies. Historical excavation and recoat information was utilized to identify static defects and quantify systemic bias between inspections. To reduce differences in reporting and the analyst interpretation of the recorded magnetic signals, novel analysis techniques were employed to normalize the data sets against each other. The resulting uncertainty of the corrosion growth rates was then further reduced by deriving, and applying a regression model to reduce the effect of the different sizing models and the identified systemic bias. The reduced uncertainty ultimately led to a better understanding of the corrosion activity on the pipeline and facilitated a better integrity management decision process.
Sponsorship	International Petroleum Technology Institute and the Pipeline Division
Starting Page	467
Ending Page	473
Page Count	7
File Format	PDF
ISBN	9780791844205
DOI	10.1115/IPC2010-31306
e-ISBN	9780791838853
Volume Number	2010 8th International Pipeline Conference, Volume 1
Conference Proceedings	2010 8th International Pipeline Conference
Language	English
Publisher Date	2010-09-27
Publisher Place	Calgary, Alberta, Canada
Access Restriction	Subscribed
Subject Keyword	Errors Uncertainty Regression models Corrosion Pipeline systems Pipelines Computer software Measurement uncertainty Inspection Signals Reliability Density
Content Type	Text
Resource Type	Article

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Applying Corrosion Growth Rates Derived From Repeat ILI Runs to Predict Future Severity

Obtaining Corrosion Growth Rates From Repeat In-Line Inspection Runs and Dealing With the Measurement Uncertainties

Detection of Active Corrosion From a Comparison of ILI Runs

Approach to Assessment of Corrosion Growth in Pipelines

Performance of In-Line Inspection Tools and Their Influence on the Structural Reliability of Corroded Pipeline

Time-Dependent Corrosion Growth Modeling Using Multiple ILI Data

Assessment of Pipeline Integrity Based on Successive Inspection Data

Application and Validation of Statistically Based Corrosion Growth Rates

Hierarchical Bayesian Corrosion Growth Model Based on In-Line Inspection Data

A Practical Application to Calculating Corrosion Growth Rates by Comparing Successive ILI Runs From Different ILI Vendors

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