Locating internal corrosion damage in gas pipelines is made difficult by the presence of large uncertainties in flow characteristics, pre-existing conditions, corrosion resistance, elevation data, and test measurements. This paper describes a preliminary methodology to predict the most probable corrosion damage location along the pipelines, and then update this prediction using inspection data. The approach computes the probability of critical corrosion damage as a function of location along the pipeline using physical models, for flow, corrosion rate, and inspection information as well as uncertainties in elevation data, pipeline geometry and flow characteristics. The probabilistic methodology is based on the internal corrosion direct assessment (ICDA) methodology. The probability of corrosion damage is the probability that the corrosion depth exceeds a critical depth times the probability of the presence of electrolytes such as water. Water is assumed present at locations where the pipeline inclination angle is greater than the critical angle. The corrosion rate is defined to be a linear combination of three candidate corrosion rate models with separate weight factors. Monte Carlo simulation and the first-order reliability method (FORM) implemented in a simple spreadsheet model are used to perform the probability integration. Bayesian updating is used to incorporate inspection information (e.g., in-line, excavation, etc.) and update the corrosion rate model weight factors and thereby refine the prediction of most probable damage location. This provides a systematic method for focusing costly inspections on only those locations with a high probability of damage while allowing future predictions to better reflect field observations.

This content is only available via PDF.
You do not currently have access to this content.