Advances in corrosion growth modeling for oil and gas pipelines: A review

Abstract

To quantify the progress of corrosion damage and develop pipeline integrity management strategies, it is necessary to establish a reliable corrosion growth model. Due to the complexity of the corrosion process, the availability of data, and the limitations of various models in their applicability, there is currently no generally accepted optimal corrosion growth prediction methodology. Corrosion data used for modeling, in-line inspection techniques for detecting defects, and sources of uncertainty in the modeling process are briefly described. This paper focuses on reviewing the concepts, the performance, and the application of existing pipeline corrosion growth models. The deterministic and probabilistic models are analyzed in detail according to the core methods involved, and the latest applications of machine learning and deep learning in corrosion growth modeling are also introduced. To leverage the strengths of various models, this paper presents hybrid approach models based on the combinations of the aforementioned models, which have greater performance and interpretability than single models and should be given more attention in the future development of corrosion growth prediction. Finally, some suggestions for future development are put forward in light of the challenges and deficiencies present in the current modeling process.