Petroleum Science and Engineering

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Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline

Received: 11 October 2019    Accepted: 05 November 2019    Published: 11 November 2019
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Abstract

The ability to detect leak is crucial in pipeline fluid transport operations. Leaks will inevitably occur in pipelines due to wide range of uncertainties. A good leak detection system should not only be able to detect leak but also accurately estimate the actual time of leak occurrence. This will enable proper estimation of the fluid loss, from the pipeline before shut-in of the pipeline or before remedial actions were carried out on the pipeline which ultimately will help quantified the degree of financial or environmental implications resulting from the leak incidence. This paper gives a new model for the estimation of the time of leak in natural gas pipeline. The idea for the model hinges on the notion that the time of response of most pipeline alarm are not necessarily the time actual time the leak occurred. Period of lapse depends on the accuracy, sophistication of the alarm system and volume of leak it is capable of detecting. Most alarm systems respond at later times than the time the leak occurred. Quantification of fluid loss volume demands that the actual time of leak occurrence be determined, this means that the time the leak occurred must be calculated accurately. The model was simulated using the Matlab software. The results show that the model is highly accurate when tested with field data.

DOI 10.11648/j.pse.20190302.15
Published in Petroleum Science and Engineering (Volume 3, Issue 2, December 2019)
Page(s) 68-73
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), 2024. Published by Science Publishing Group

Keywords

Leak Detection, Time of Leak, Pipeline Rupture, Pressure Wave

References
[1] Nicholas, E., Carpenter, P., Henrie, M., Hung, D., Kundert, C. (2017). A New Approach to Testing Performance of a Pipeline Leak Detection System. Paper prepared for presentation at the PSIG Annual Meeting held in Atlanta, Georgia, USA.
[2] Hanmer, G., Mora, V., Fábio C. G., Sergio L. (2018). Modelling of Rapid Transients in Natural Gas Pipelines. Paper prepared for presentation at the PSIG Annual Meeting held in Deer Valley, Utah.
[3] Baltazar, S. T. and Azevedo Perdicoúlis, T-P and Lopes dos Santos, P. (2016). Quadripole Models for Simulation and Leak Detection on Gas Pipelines. Paper prepared for presentation at the PSIG Annual Meeting held in Vancouver, British Columbia.
[4] Siebenaler Shane, Krishnan Venkat, Nielson Jordan Edlebeck, John. (2017). Fiber-Optic Acoustic Leak Detection for Multiphase Pipelines. Proceedings of the Twenty-seventh (2017) International Ocean and Polar Engineering Conference San Francisco, CA, USA.
[5] Kegang Ling, Guoqing Han, X. N, Chunming Xu, Jun He, Peng Pei, and Jun Ge. (2015): A New Method for Leak Detection in Gas Pipelines, Paper (SPE 1891568) accepted for presentation at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver.
[6] Qian, D. and Fox, P. H. and See, B. L., (2015). Accurate Natural Gas Load Hourly Forecasting Using ANN Model Trained with Multiple Parameters’. 46th PSIG Annual Meeting, New Orleans, LA, USA.
[7] Reynolds Joe, Kam Amy (2019). An Evaluation of Negative Pressure Wave Leak Detection: Challenges, Limitations, and Use Cases.
[8] Dinis J. M., Wojtanowicz A. K. and Scott S. L. (1999): Leak detection in liquid subsea flowlines with no recorded feedrate. J. Energy Res. Tech., Vol. 121, No. 3, pp. 161–166.
[9] Verde C. (2005): Accommodation of multi-leak location in a pipeline. Control Engineering. Practice, Vol. 13, No. 8, pp. 1071–1078.
[10] Zhao Q. and Zhou D. H. (2001): Leak detection and location of gas pipelines based on a strong tracking filter. — Trans. Contr. Automat. Syst. Eng., Vol. 3, No. 2, pp. 89–94.
[11] Hauge, E., Aamo, O. M., and Godhavn, J.-M. (2009): Model-Based Monitoring and Leak Detection in Oil and Gas Pipelines. SPE Proj Fac & Const 4 (3): 53–60. SPE-114218-PA.
[12] Schlumberger. 2014. OLGA Dynamic Multiphase Flow Simulator. http:// www.software.slb.com/products/foundation/Pages/olga.aspx.
[13] Balda Rivas, K. V. and Civan, F. (2013): Application of Mass Balance and Transient Flow Modeling for Leak Detection in Liquid Pipelines. Presented at the SPE Production and Operations Symposium, Oklahoma City, Oklahoma, USA, 23–26 March. SPE-164520-MS.
[14] Jin Mingang (2019). Investigation on Parameters Affecting the Performance of Negative Pressure Wave Leak Detection Systems. Paper prepared for presentation at the PSIG Annual Meeting held in London, England.
[15] Tian, Chun Hua, Jun Chi Yan, Jin Huang, Yu Wang, DongSup Kim, and Tongnyoul Yi. (2012) "Negative pressure wave based pipeline leak detection: Challenges and algorithms." In Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on, pp. 372-376. IEEE.
Author Information
  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

  • Department of Petroleum Engineering, Federal University of Technology, Owerri (FUTO), Nigeria

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  • APA Style

    Obibuike Ubanozie Julian, Ekwueme Stanley Toochukwu, Ohia Nnaemeka Princewill, Igbojionu Anthony Chemazu, Igwilo Kevin Chinwuba, et al. (2019). Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline. Petroleum Science and Engineering, 3(2), 68-73. https://doi.org/10.11648/j.pse.20190302.15

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    ACS Style

    Obibuike Ubanozie Julian; Ekwueme Stanley Toochukwu; Ohia Nnaemeka Princewill; Igbojionu Anthony Chemazu; Igwilo Kevin Chinwuba, et al. Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline. Pet. Sci. Eng. 2019, 3(2), 68-73. doi: 10.11648/j.pse.20190302.15

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    AMA Style

    Obibuike Ubanozie Julian, Ekwueme Stanley Toochukwu, Ohia Nnaemeka Princewill, Igbojionu Anthony Chemazu, Igwilo Kevin Chinwuba, et al. Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline. Pet Sci Eng. 2019;3(2):68-73. doi: 10.11648/j.pse.20190302.15

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  • @article{10.11648/j.pse.20190302.15,
      author = {Obibuike Ubanozie Julian and Ekwueme Stanley Toochukwu and Ohia Nnaemeka Princewill and Igbojionu Anthony Chemazu and Igwilo Kevin Chinwuba and Kerunwa Anthony},
      title = {Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline},
      journal = {Petroleum Science and Engineering},
      volume = {3},
      number = {2},
      pages = {68-73},
      doi = {10.11648/j.pse.20190302.15},
      url = {https://doi.org/10.11648/j.pse.20190302.15},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.pse.20190302.15},
      abstract = {The ability to detect leak is crucial in pipeline fluid transport operations. Leaks will inevitably occur in pipelines due to wide range of uncertainties. A good leak detection system should not only be able to detect leak but also accurately estimate the actual time of leak occurrence. This will enable proper estimation of the fluid loss, from the pipeline before shut-in of the pipeline or before remedial actions were carried out on the pipeline which ultimately will help quantified the degree of financial or environmental implications resulting from the leak incidence. This paper gives a new model for the estimation of the time of leak in natural gas pipeline. The idea for the model hinges on the notion that the time of response of most pipeline alarm are not necessarily the time actual time the leak occurred. Period of lapse depends on the accuracy, sophistication of the alarm system and volume of leak it is capable of detecting. Most alarm systems respond at later times than the time the leak occurred. Quantification of fluid loss volume demands that the actual time of leak occurrence be determined, this means that the time the leak occurred must be calculated accurately. The model was simulated using the Matlab software. The results show that the model is highly accurate when tested with field data.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Mathematical Model for Time of Leak Estimation in Natural Gas Pipeline
    AU  - Obibuike Ubanozie Julian
    AU  - Ekwueme Stanley Toochukwu
    AU  - Ohia Nnaemeka Princewill
    AU  - Igbojionu Anthony Chemazu
    AU  - Igwilo Kevin Chinwuba
    AU  - Kerunwa Anthony
    Y1  - 2019/11/11
    PY  - 2019
    N1  - https://doi.org/10.11648/j.pse.20190302.15
    DO  - 10.11648/j.pse.20190302.15
    T2  - Petroleum Science and Engineering
    JF  - Petroleum Science and Engineering
    JO  - Petroleum Science and Engineering
    SP  - 68
    EP  - 73
    PB  - Science Publishing Group
    SN  - 2640-4516
    UR  - https://doi.org/10.11648/j.pse.20190302.15
    AB  - The ability to detect leak is crucial in pipeline fluid transport operations. Leaks will inevitably occur in pipelines due to wide range of uncertainties. A good leak detection system should not only be able to detect leak but also accurately estimate the actual time of leak occurrence. This will enable proper estimation of the fluid loss, from the pipeline before shut-in of the pipeline or before remedial actions were carried out on the pipeline which ultimately will help quantified the degree of financial or environmental implications resulting from the leak incidence. This paper gives a new model for the estimation of the time of leak in natural gas pipeline. The idea for the model hinges on the notion that the time of response of most pipeline alarm are not necessarily the time actual time the leak occurred. Period of lapse depends on the accuracy, sophistication of the alarm system and volume of leak it is capable of detecting. Most alarm systems respond at later times than the time the leak occurred. Quantification of fluid loss volume demands that the actual time of leak occurrence be determined, this means that the time the leak occurred must be calculated accurately. The model was simulated using the Matlab software. The results show that the model is highly accurate when tested with field data.
    VL  - 3
    IS  - 2
    ER  - 

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