Corrosion Monitoring and Prediction in Oil and Gas Pipelines Using Machine Learning

Abstract

Oil and gas pipelines are susceptible to corrosion, which can lead to leaks and environmental hazards. This research aims to develop a machine learning-based approach for corrosion monitoring and prediction in pipelines. By analyzing various data sources, including pipeline characteristics, environmental factors, and inspection data, the model will be able to identify potential corrosion hotspots and predict the likelihood of failure. This will enable proactive maintenance and reduce the risk of costly incidents