Pipelines are an important part of offshore oil and gas field development facilities and the main means of gathering and transporting offshore oil and gas resources. However, pipelines are subject to deterioration and degradation in the corrosion media. Corrosion risk assessment and prediction is an effective way to avoid leakage of oil and gas field pipelines and facilities, ensure safe operation and save cost. Hence, in this study, a machine learning model with excellent predictive performance was constructed for corrosion rate, to provide an effective mean for processing complex corrosion data and to provide a useful tool for further exploration of submarine pipeline corrosion problems. Meanwhile, a method that can effectively and quickly evaluate the accuracy of corrosion rate prediction model was explored, which can be used as a reference to select the most appropriate and accurate Machine Learning (ML) model based on existing data.

Subject
Field performance, Test methods, Modeling, Corrosion rate, Predicted corrosion rate, Machinery, Pipelines, Oil and gas, Algorithms, Corrosion modeling, Corrosion data, Risk management, Training
Keywords:
Machine Learning, Statistical Indicator, Corrosion, Database, Global Performance Indicator
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2022
Association for Materials Protection and Performance (AMPP)
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