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

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.

Automated summary

In order to quantify corrosion damage and develop effective pipeline integrity management strategies, a reliable corrosion growth model is necessary. However, there is currently no generally accepted optimal method for predicting corrosion growth due to the complexity of the corrosion process, data availability issues, and limitations of existing models. This paper reviews the concepts, performance, and application of existing pipeline corrosion growth models, analyzes deterministic and probabilistic models in detail, and introduces the latest applications of machine learning and deep learning in corrosion growth modeling. Hybrid approach models, which combine various models, are proposed as they offer better performance and interpretability than single models and should be focused on in future corrosion growth prediction development. Suggestions for future development are also provided to address challenges and deficiencies in the current modeling process.