A method of data-driven risk analysis has been developed and applied to improve the efficiency and effectiveness of inspecting surface corrosion of SS316 piping on the Gjøa platform on the Norwegian Continental Shelf. Using a probabilistic statistics method, in conjunction with a new digitized tool for inspection, reporting and evaluation, inspection data on external pitting and crevice corrosion on each line were analyzed and used as the basis for estimating corrosion development rates to be expected for the coming years. The method uses advanced software and interative computation to calculate the risk of leakage of each line, which, in combination with the criticality of the pipe, provides a target schedule for the next inspection of each line. In effect, the data driven model is an intelligent agent that generates a statistically based inspection program used to manage the risks of line failure while inspecting lines only when they need to be inspected.
In the case study presented here, the asset owner was able to implement a schedule to reduce SS316 piping inspection by 70% over four years compared to a conventional risk based inspection calling for more frequent inspection of the SS316 lines.