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Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey

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Data Science and Big Data: An Environment of Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 24))

Abstract

The applicability of intelligent techniques for the safety assessment of oil and gas pipelines is investigated in this study. Crude oil and natural gas are usually transmitted through metallic pipelines. Working under unforgiving environments, these pipelines may extend to hundreds of kilometers, which make them very susceptible to physical damage such as dents, cracks, corrosion, etc. These defects, if not managed properly, can lead to catastrophic consequences in terms of both financial losses and human life. Thus, effective and efficient systems for pipeline safety assessment that are capable of detecting defects, estimating defects sizes, and classifying defects are urgently needed. Such systems often require collecting diagnostic data that are gathered using different monitoring tools such as ultrasound, magnetic flux leakage , and Closed Circuit Television (CCTV) surveys. The volume of the data collected by these tools is staggering. Relying on traditional pipeline safety assessment techniques to analyze such huge data is neither efficient nor effective. Intelligent techniques such as data mining techniques, neural networks , and hybrid neuro-fuzzy systems are promising alternatives. In this paper, different intelligent techniques proposed in the literature are examined; and their merits and shortcomings are highlighted.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Ahmed Bin Mohamed Military College, P. O. Box 22713, Doha, Qatar

    Abduljalil Mohamed & Mohamed Salah Hamdi

  2. Department of Electrical and Computer Engineering, Concordia University, 1515 St. Catherine W, Montreal, Canada

    Sofiène Tahar

Authors
  1. Abduljalil Mohamed
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  2. Mohamed Salah Hamdi
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  3. Sofiène Tahar
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Correspondence to Abduljalil Mohamed .

Editor information

Editors and Affiliations

  1. Electrical & Computer Engineering, University of Alberta Electrical & Computer Engineering, Edmonton AL, Canada

    Witold Pedrycz

  2. Dept of CS and Information Engineering, National Taiwan Univ of Science and Tech Dept of CS and Information Engineering, Taipei, Taiwan

    Shyi-Ming Chen

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Mohamed, A., Hamdi, M.S., Tahar, S. (2017). Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey. In: Pedrycz, W., Chen, SM. (eds) Data Science and Big Data: An Environment of Computational Intelligence. Studies in Big Data, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-53474-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-53474-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53473-2

  • Online ISBN: 978-3-319-53474-9

  • eBook Packages: EngineeringEngineering (R0)

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