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A hybrid population-based degradation model for pipeline pitting corrosion

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  • Heidary, Roohollah
  • Groth, Katrina M.

Abstract

This paper presents a novel algorithm to develop a population-based pitting corrosion degradation model for piggable oil and gas pipelines. The algorithm is designed to estimate and predict the distribution of actual depth of existing pits on a pipeline segment, given two or more sets of in-line inspection data that have uncertainty in size and number of the detected pits. This algorithm eliminates the need for a defect-matching procedure for those pits that are not critical, that is required in developing defect-based pitting corrosion degradation models. A hierarchical Bayesian model based on a non-homogeneous gamma process is developed to fuse the uncertain in-line inspection data and physics of failure knowledge of pitting corrosion process. Measurement error (ME), probability of detection (POD), and probability of false call (POFC) are addressed in the developed algorithm. The application of the developed algorithm is demonstrated by implementing it on a simulated case study and the results are compared with the simulated data from a generic degradation model that is available in the literature. Results indicate that this algorithm can predict the degradation level of the pipeline with a high accuracy.

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  • Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002726
    DOI: 10.1016/j.ress.2021.107740
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    References listed on IDEAS

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    Cited by:

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    6. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).

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