Abstract

There are several difficulties that come with maintaining the structure and integrity of subsea pipelines. Some of these difficulties are caused by the extreme weather conditions and other issues during operations, which greatly speed up the erosion of the pipeline. These pipelines are of extreme importance when it comes to extracting oil and gas, yet the breakdown of the pipes is often overlooked. While traditional risk assessments or erosion control measures are important, they tend to focus on reactive measures to slow given periods of erosion, instead of predictive measures. This navigates predicting pipeline evaluations with the help of an advanced machine learning structure. The model uses Remote Operated Vehicle Inspection Reports, Cathodic Protection Survey Reports, and Maintenance Registries to produce an informed estimate with regard to disintegration, such as loss of armor, coating, and corrosion fatigue. Supervised learning Support Vector Machines and Random Forest techniques are applied to provide a more informed revolutionary learning experience, as opposed to traditional learning methods. The predictive model relies on empirical assessments that are measured in vast quantities in sea pipelines and are compared with ground truths to be validated. The design of the model uses sets of features that are relevant to sea pipes. The results demonstrate the novel model adds value to pre-existing methods used by risk, as it relies on the machine learning process, predicting more accurately sooner and with less inaccuracies. In addition, the analysis of the importance of individual features associated with the seabed topography, cath protection potential, and operating pressure for their value in proactive maintenance presents actionable insights and contextual value. This shows the value of machine learning in moving the management of pipeline integrity from a reactive to a more proactive approach, which in turn, improves reliability, minimizes downtime, and reduces inspection costs. This framework enhances the ability for decision-making based on evidence while fulfilling the core aim of deep-water energy production, which is the utmost safety and ecological integrity. The focus of the next stage of the project will be on the deployment of the model to varying offshore conditions and incorporating real-time data from sensors for interval-based assessment.