Accounting for Corrosion Growth and Interaction in Future Severity Assessments

Abstract The determination of corrosion growth rates and their application in the prediction of future severity is a critical part of pipeline integrity management. Accurate corrosion growth rates are needed to predict pipeline reliability as a function of time, to identify the need for and timing of field investigations and/or repairs and to determine optimum re-inspection intervals. The consequences associated with both underestimating and overestimating growth rates can be significant in terms of both safety and resource performance. The use of repeat ILI data to match and compare metal loss sites in order to estimate the corrosion growth rates at individual defects along a pipeline is a well-used and established practice in the industry. There are many ways that corrosion growth rates can be used in future integrity predictions with most approaches only accounting for corrosion growth in the depth dimension taking no account of surface area growth and potential interactions between adjacent corrosion areas over time. Now that we have a wealth of historical data with 3, 4 or even 5 sets of ILI data for the same pipelines we are able to experiment with more advanced three-dimensional modelling and have the ability to test new approaches vs actual “truth” data. With the benefit of this progressive viewpoint, the methodologies employed for evaluating and applying ILI based corrosion rates are being further improved and refined to give more accurate predictions of the future pipeline condition, the response schedule and for setting the timing of re-inspections. This paper describes a new approach that predicts how areas of corrosion can increase in depth over-time and in surface area by combining with surrounding corrosion defects and with newly developing defects. The main differences between this approach and the more commonly used approaches are: • It does not use fixed individual defect or pipe spool growth rates, but instead uses local growth rate distributions drawn from groups of corrosion defects under the same or very similar corrosive environments. • The assessment accounts for growth in all three dimensions (depth, length and width) and models the more complex interaction between nearby areas of corrosion. • It uses a machine learning model to predict the location and pattern of potential new corrosion sites. This new approach is illustrated and compared to the more commonly used methodologies via the use of case studies on real ILI data sets.