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Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System

Received: 17 January 2023     Accepted: 14 February 2023     Published: 15 April 2023
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Abstract

Gas hydrates account for a huge flow assurance encounter in the passage of natural gas through pipelines. Its undesirability stems from the fact that these solids reduce pipe diameter open to gas flow, and challenge pipeline integrity, therefore leading to bursting pipes and increasing costs. Hydrates undergo four phases of development: entrainment, growth, agglomeration and plugging – and do not usually constitute a flow assurance challenge until agglomeration. These challenges are even more pronounced in the presence of condensate in the pipeline. This study was therefore designed by developing a predictive model of the hydrate growth initiation point along the pipeline where hydrates start to form in the presence of gas, condensate, and water. The developed predictive analytical model at which quasi liquid layer starts to form on the hydrate seed relates the quasi-liquid layer temperature to the gas hydrate mass, pipeline length, induction time, hydrate percentage in the fluid composition, hydrate density, change in enthalpy and the flowing hydrate velocity in the pipe system. The developed predictive model will assist in identifying when heating of pipelines can be done to control hydrate formation by keeping the temperature above the quasi-liquid layer temperature. This predictive model was in concordance with field observation.

Published in Petroleum Science and Engineering (Volume 7, Issue 1)
DOI 10.11648/j.pse.20230701.13
Page(s) 14-21
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Gas Hydrate Growth, Gas-Condensate, Hydrate Temperature, Maturation Stages, Predictive Model

References
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[2] Sloan E. Dendy, Koh Carolyn Ann. (2008). Clathrate hydrates of natural gases. Third edition. CRC Press, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, Florida 33487-2742, U.S.A.
[3] Steed Jonathan W, Atwood Jerry L. (2013). Supramolecular Chemistry. 2nd edition, Wiley.
[4] Sloan E. Dendy, Koh Carolyn Ann, Sum K. Amadeu, Ballad A. L., Shoup, G. J… & Palermo Thierry. (2009). Hydrates: State of the Art Inside and Outside Flow lines. Journal of Petroleum Technology 61 (12): 89-94. doi: 10.2118/118534-MS.
[5] [5] Haghighi H., Azarinezhad R., Chapoy A., Anderson R., Tohidi B. (2007). Hydraflow: Avoiding Gas Hydrate Problems, SPE 107335, 11th-14th June, London, United Kingdom. doi.org/10.2118/107335-MS.
[6] Akinsete Oluwatoyin Olakunle, Isehunwa Sunday Oloruntoba (2015). Novel Analytical Model for Predicting Hydrate Formation Onset Pressures in Natural Gas Pipeline Systems. Journal of Characterization and Development of Novel Materials 7 (4): 605-619.
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[12] Kazunori Okutani, Yui Kuwabara, Yasuhiko H. Mor. (2008). Surfactant Effects on Hydrate Formation in an Unstirred Gas/Liquid System: An Experimental Study Using Methane and Sodium Alkyl Sulfates, Chemical Engineering Science, 63 (1): 183-194. doi.org/10.1016/j.ces.2007.09.012.
[13] Huo Z., Freer E., Lamar M., Sannigrahi B., Knauss D. M., Sloan E. D. (2001). Hydrate plug prevention by anti-agglomeration. Chemical Engineering Science. 56 (17): 4979–4991.
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Cite This Article
  • APA Style

    Akinsete Oluwatoyin, Obode Elizabeth, Isehunwa Sunday. (2023). Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System. Petroleum Science and Engineering, 7(1), 14-21. https://doi.org/10.11648/j.pse.20230701.13

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    ACS Style

    Akinsete Oluwatoyin; Obode Elizabeth; Isehunwa Sunday. Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System. Pet. Sci. Eng. 2023, 7(1), 14-21. doi: 10.11648/j.pse.20230701.13

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    AMA Style

    Akinsete Oluwatoyin, Obode Elizabeth, Isehunwa Sunday. Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System. Pet Sci Eng. 2023;7(1):14-21. doi: 10.11648/j.pse.20230701.13

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  • @article{10.11648/j.pse.20230701.13,
      author = {Akinsete Oluwatoyin and Obode Elizabeth and Isehunwa Sunday},
      title = {Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System},
      journal = {Petroleum Science and Engineering},
      volume = {7},
      number = {1},
      pages = {14-21},
      doi = {10.11648/j.pse.20230701.13},
      url = {https://doi.org/10.11648/j.pse.20230701.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20230701.13},
      abstract = {Gas hydrates account for a huge flow assurance encounter in the passage of natural gas through pipelines. Its undesirability stems from the fact that these solids reduce pipe diameter open to gas flow, and challenge pipeline integrity, therefore leading to bursting pipes and increasing costs. Hydrates undergo four phases of development: entrainment, growth, agglomeration and plugging – and do not usually constitute a flow assurance challenge until agglomeration. These challenges are even more pronounced in the presence of condensate in the pipeline. This study was therefore designed by developing a predictive model of the hydrate growth initiation point along the pipeline where hydrates start to form in the presence of gas, condensate, and water. The developed predictive analytical model at which quasi liquid layer starts to form on the hydrate seed relates the quasi-liquid layer temperature to the gas hydrate mass, pipeline length, induction time, hydrate percentage in the fluid composition, hydrate density, change in enthalpy and the flowing hydrate velocity in the pipe system. The developed predictive model will assist in identifying when heating of pipelines can be done to control hydrate formation by keeping the temperature above the quasi-liquid layer temperature. This predictive model was in concordance with field observation.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Predictive Analytical Model for Hydrate Growth Initiation Point in Multiphase Pipeline System
    AU  - Akinsete Oluwatoyin
    AU  - Obode Elizabeth
    AU  - Isehunwa Sunday
    Y1  - 2023/04/15
    PY  - 2023
    N1  - https://doi.org/10.11648/j.pse.20230701.13
    DO  - 10.11648/j.pse.20230701.13
    T2  - Petroleum Science and Engineering
    JF  - Petroleum Science and Engineering
    JO  - Petroleum Science and Engineering
    SP  - 14
    EP  - 21
    PB  - Science Publishing Group
    SN  - 2640-4516
    UR  - https://doi.org/10.11648/j.pse.20230701.13
    AB  - Gas hydrates account for a huge flow assurance encounter in the passage of natural gas through pipelines. Its undesirability stems from the fact that these solids reduce pipe diameter open to gas flow, and challenge pipeline integrity, therefore leading to bursting pipes and increasing costs. Hydrates undergo four phases of development: entrainment, growth, agglomeration and plugging – and do not usually constitute a flow assurance challenge until agglomeration. These challenges are even more pronounced in the presence of condensate in the pipeline. This study was therefore designed by developing a predictive model of the hydrate growth initiation point along the pipeline where hydrates start to form in the presence of gas, condensate, and water. The developed predictive analytical model at which quasi liquid layer starts to form on the hydrate seed relates the quasi-liquid layer temperature to the gas hydrate mass, pipeline length, induction time, hydrate percentage in the fluid composition, hydrate density, change in enthalpy and the flowing hydrate velocity in the pipe system. The developed predictive model will assist in identifying when heating of pipelines can be done to control hydrate formation by keeping the temperature above the quasi-liquid layer temperature. This predictive model was in concordance with field observation.
    VL  - 7
    IS  - 1
    ER  - 

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Author Information
  • Department of Petroleum Engineering, Faculty of Technology, University of Ibadan, Ibadan, Nigeria

  • Department of Petroleum Engineering, Faculty of Technology, University of Ibadan, Ibadan, Nigeria

  • Department of Petroleum Engineering, Faculty of Technology, University of Ibadan, Ibadan, Nigeria

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