Pitting corrosion under insulation is one of the challenging issues for safe operation of offshore facilities. Degradation usually remains hidden causing the inspection of insulated assets to be equally challenging. The modeling of the pitting corrosion under insulation (CUI) helps us to better understand the current state of the asset and predict failure. This paper investigates the factors affecting the pit initiation and pit growth on equipment under insulation operating in offshore environments. A methodology is proposed for studying the pitting CUI characteristics, including pit initiation time, pit density, and maximum pit depth over time. The proposed methodology provides a practical and more effective asset life management approach when supported by inspection data. The practical application of the proposed methodology is demonstrated in this paper using a pressure vessel case study in an offshore platform.
This paper presents a methodology to assess and dynamically update the risk of process components affected by pitting corrosion. The proposed framework considers the time-dependent growth of pits and uses the non-homogenous Markov process to model the maximum pit depth. The developed pit depth model is incorporated into a limit state function to estimate the failure probability of affected components. Economic consequences are estimated considering both business and accidental losses due to failure. The estimated risk is updated using Bayesian analysis as new inspection data become available. Different risk management strategies including prevention, control and mitigation measures are also studied.
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