Predicting Corrosion Growth and Reliability of the Oil Pipeline

• Published in: Proceedings. Annual Reliability and Maintainability Symposium pp. 1 - 6
• Main Authors: Alqarni, Abdulsalam Ahmed, Prakash Yadav, Om, Nepal, Bimal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.05.2021
• Subjects: Analytical models; Corrosion; corrosion rate; Inspection; linear model; Monte Carlo method; Oils; pipeline; Pipelines; Predictive models; Random access memory; reliability analysis
• ISSN: 2577-0993
• DOI: 10.1109/RAMS48097.2021.9605725

SUMMARY & CONCLUSIONSThis paper proposes a general model to predict the corrosion growth process over time in the pipeline and combined with the Monte Carlo simulation (MCS) to assess the pipeline's reliability. Although several models have been proposed to capture corrosion behavior in the pipeline, the linear corrosion growth rate model is widely used due to its simplicity and capability to predict the corrosion process over time despite the scarcity of more detailed pipeline information. Therefore, we propose a linear model, but it differs from the existing linear corrosion growth models where it is applicable to include multiple in-line inspection datasets. The proposed model is called multiple in-line inspections (M-ILIs) based-linear corrosion rate model. Also, this paper is conducting a comparative study of the M-ILIs model with the other three common existing linear models, such as single-value (SV) corrosion rate model, single in-line inspection (S-ILI) based-linear corrosion rate model, and two in-line inspections (T-ILIs) based-linear corrosion rate model. The results demonstrate the superiority of the proposed model over other models, especially for long-term prediction, where it gives more accurate prediction outcomes, which makes the M-ILIs model encouraging and could be useful for reliability analysts and managers to utilize it in corrosion prediction regarding buried oil and gas pipelines.