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A modified method for the safety factor parameter: The use of big data to improve petroleum pipeline reliability assessment

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  • Zhang, Hewei
  • Dong, Shaohua
  • Ling, Jiatong
  • Zhang, Laibin
  • Cheang, Brenda

Abstract

Due to the potential severity of oil and gas pipeline accidents, accurate assessments on the reliability and viability of pipelines in the petroleum industry is of paramount importance. Nevertheless, the safety factor (SF) parameter in some well-established assessment standards are limited in their applications. This paper proposes a modified method for the SF parameter to better assess petroleum pipeline reliability. The proposed method improves upon current methods in that the SF is derived from multiple critical factors based on pipeline big data rather than the calculation of only the pressure of the pipeline. Data from an in-service pipeline is used as a case study to demonstrate how the proposed modified SF parameter is calculated. Comparative analysis with the existing method's results provide clear evidence that the proposed modification method is more accurate as it shows how the SF parameter changes according to different regional levels. This modified method, which incorporates Correlation Analysis, Mutual Information Principal Component Analysis (MIPCA), and Weighted Aggregated Sum Product Assessment (WASPAS), is in accordance with the widely accepted American Society of Mechanical Engineers (ASME) Manual for Determining the Remaining Strength of Corroded Pipelines (B31G-2012). With that said, the effectiveness of our modified method is directly related to the factors and case-based values being used. Therefore, although generally applicable to any pipeline, any form of SF analytics must be on a case-by-case basis.

Suggested Citation

  • Zhang, Hewei & Dong, Shaohua & Ling, Jiatong & Zhang, Laibin & Cheang, Brenda, 2020. "A modified method for the safety factor parameter: The use of big data to improve petroleum pipeline reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s0951832019309263
    DOI: 10.1016/j.ress.2020.106892
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    References listed on IDEAS

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    1. Kar, Alpa & Chatterjee, Sucharita & Ghosh, Dipak, 2019. "Multifractal detrended cross correlation analysis of Land-surface temperature anomalies and Soil radon concentration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 236-247.
    2. Khaled El-Akruti & Tieling Zhang & Richard Dwight, 2016. "Maintaining pipeline integrity through holistic asset management," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(5), pages 618-638.
    3. Mohsen Pirdashti & Madjid Tavana & Mimi Haryani Hassim & Majid Behzadian & I.A. Karimi, 2011. "A taxonomy and review of the multiple criteria decision-making literature in chemical engineering," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 1(4), pages 407-467.
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    Cited by:

    1. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Lingyun, Guo & Markus, Niffenegger & Jing, Zhou, 2022. "A novel procedure to evaluate the performance of failure assessment models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. 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).
    7. Yang, Yang & Li, Suzhen & Zhang, Pengcheng, 2022. "Data-driven accident consequence assessment on urban gas pipeline network based on machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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