Validation of Corrosion Growth Rate Models

ABSTRACT

Corrosion growth rate (CGR) is one of the key elements in corrosion threat management. It is used to determine metal loss In-Line Inspection (ILI) re-assessment interval, identify high growth features that require excavation before the next ILI and predict probability of failure of an unmitigated feature. Overestimated CGR may result in unnecessary expenditures for excavations and remediation, while underestimated CGR could result in pipeline failure. Although there are many methods to derive CGR from successive ILIs, there is no particular approach widely accepted by industry. In this paper, several CGR methodologies used by Enbridge to manage corrosion threats on pipelines including CGR based on feature matching and signal matching are discussed and validated. CGR based on feature matching refers to the rate that is calculated based on ILI reported depths of two back-to-back ILIs. CGR based on signal matching refers to the rate that is determined based on ILI raw data review provided from the ILI vendor. Both feature level and joint level CGR have been used in corrosion threat management. A probabilistic CGR methodology based on Pipeline Research Council International (PRCI)⁽¹⁾ EC-1-2 project (Development of detailed procedures for comparing successive ILI runs to establish corrosion growth rate) that accounts for uncertainty of measurement tools is also included in this study.

The ILI data and field non-destructive evaluation (NDE) results of six onshore pipelines are used for this study. Each of the six pipelines has at least three high resolution Magnetic Flux Leakage (MFL) inspections. The various CGRs are established based on the 1^st and 2^nd inspections. The CGR methodologies are validated based on comparing the predicted depth using CGR with the field measured depth as well as the depth reported by the 3^rd ILI. Over 700 field measured feature depths are used in this study and over 60,000 ILI features are used to validate the estimated depth at the time of field measurements and at the time of the 3^rd ILI. This paper demonstrates the validation results of feature level and joint level CGR based on both feature matching and signal matching. The probabilistic CGR methods are also validated using the same method. The CGR validation study enables pipeline operators to establish defect repair schedules and re-inspection intervals with increased confidence.