Integrating the root cause analysis to machine learning interpretation for predicting future failure

The research proposes a new model for evaluating offshore pipelines due to corrosion. The existing inspection method has an inherent limitation in reusing the primary root cause analysis data to forecast the potential loss and corrosion mitigation, particularly in the scope of data utilization. The study implements Artificial Intelligence to transfer the knowledge of failure analysis as a consideration for conducting the inspection and lowering the risk of failure. This work combines experimental and modelling methodologies to assert the actual and feasible inspection method. The elemental composition, hardness, and tensile tests are utilized to unveil the types of corrosion products and metallic properties. Scanning Electronic Microscope and Energy Dispersive X-Ray (SEM-EDX) and X-Ray Diffractometer (XRD) was utilized to assess the corrosion product and their corresponding morphology to reveal the corrosion mechanism. The Gaussian Mixture Model (GMM), aided by the Pearson Multicollinear Matrix, shows the typical risk and predicts the damage mechanism of the spool to suggest the types of mitigation scenarios for the pipeline's longevity. According to the laboratory result, the wide and shallow pit corrosion and channelling are evident. The result of the tensile and hardness test confirms the types of the API 5 L X42 PSL 1 standard material. The SEM-EDX and XRD provide a piece of clear evidence into the corrosion product are primarily due to CO(2) corrosion. The silhouette score agrees well with the results of the Bayesian information criterion of GMM to show three different risk levels low, medium, and high-risk profiles. The combination of injection of chemicals such as parasol, biocide and cleaning pigging are a few solutions to address CO(2) corrosion. This work can be used as a guideline for assessing and clustering the risk based on the risk-based inspection.