Pipeline Integrity Analysis through Data-Driven Approaches

Pipelines are the most widely used energy transportation infrastructure in the world. Pipeline failures caused by aging, corrosion, cracks, and damages may result in irreparable societal, economic, and environmental consequences. Therefore, pipeline safety and integrity are crucial to the robustness of modern societies. Robust failure models have the potential to effectively reduce the emergent risk and the probability of hazards in pipeline construction, and extend the service life of pipeline infrastructure. In this paper, we apply a data-driven approach, Gaussian Process Regression (GPR), to predict the failure of the oil pipeline. We train and test the models based on historical public data from a report on European cross-country oil pipelines. The best model type is selected with different variable combinations. Variables such as pipeline diameter, service, and gross spillage volume are considered in the models. The data-driven pipeline failure model will provide pipeline operators with an effective way to evaluate pipeline conditions and guide pipeline rehabilitation and maintenance. The model can also be used to support pipeline integrity management based on the ASME code, which can extend the service life of the pipeline infrastructure and bring benefits to the environment and the economy.