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Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector

Received: 9 May 2016     Accepted: 21 May 2016     Published: 6 June 2016
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Abstract

In this research, Markov theoretical approach (MTA) was used to forecast the severity of risk workers were exposed to in the oil and gas industry and to determine the average period of time it would take workers to get exposed to menace of less severity and the possibility of transiting to a state where risk is high. The perils were classified into four states which include: catastrophic, critical, marginal and negligible. A solution procedure for addressing industrial hazards was developed from Markov. Fifty (50) workers in Warri Refining and Petrochemical Company (WRPC) were randomly selected for the purpose of questionnaire administration. Analysis of the data was carried out using QM software. The results showed that 56.66% of workers in marginal state would likely move to catastrophic state, while 43.34% of workers in marginal state would probably transit to critical state. Also 41.32% of workers in negligible state would move to a catastrophic state, while 58.68% of workers in negligible state would likely move to critical state within an average period of 2 to 3 years. It is therefore recommended that provision of personal protective equipment and appropriate healthcare facilities be made, risk assessment of all workers be continuously carried out; workers must be properly trained on regular basis and the enforcement and strengthening of existing legislation effectively carried out to dispel these hazards.

Published in International Journal of Mechanical Engineering and Applications (Volume 4, Issue 3)
DOI 10.11648/j.ijmea.20160403.11
Page(s) 103-108
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), 2016. Published by Science Publishing Group

Keywords

Markov Chain, Catastrophic State, Critical State, Negligible State, Marginal State

References
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[2] Faith, E. (2014). Evaluation of Occupational Health Hazards among Oil Industry Workers, Journal of Environmental Science, Toxicology and Food Technology, 8, 22-53.
[3] Osahon I. A. (2012). Statistical assessment of the potential factors affecting delayed incident reporting in the oil and gas industry. Indian Journal of occupational and Environmental Medicine, 16, 85-105.
[4] Berenice, I. G. F. (2006). Occupational Health – a requirement for development. World Health forum, 19, 60-67.
[5] Monday, O. A. (2013). Occupational health and safety in the oil and gas industry, Journal of Research in National Development JORIND, 11(2).
[6] Achojakimoni (2004). Hazards control measurement. Journal of engineering and emerging trend, 26, 34-47.
[7] Akpofure, R. (2014). A text on Safety management in the process Industry. 270-280.
[8] Amol, P. (2011). Hazard identification and risk analysis in mining industry, Masters Degree Thesis in Mining Engineering, National Institute of Technology, Rourkela.
[9] Jeong, K., Lee, D., Lee, K. & Lim H., (2007). A qualitative identification and analysis of hazards, risks and operating procedures for a decommissioning safety assessment of a nuclear research reactor, Annals of Nuclear Energy, 35, 1954-1962.
[10] Paul (2002), Occupational Health and Safety in the Oil and Gas Industry in Nigeria. Journal of Safety Engineering, 11, 25-30.
[11] Jelemenesky, L., Harisova, J. and Markos, J., (2003). Reliable risk estimation in the risk analysis of chemical industry case study: Ammonia storage pressurized spherical tank. 30th International Conference of the Slovak Society of Chemical Engineering, 58, 48-54.
[12] Dziubinski, M., Fratczak, M. and Markowski, A. S., (2006), Pipeline failure and its possible consequences. Journal of Loss Prevention in the Process Industries, 19, 399-408.
[13] Igboanugo A. C. (2010). Markov Chain Analysis of Accident Data: The Case of an Oil and Gas Firm in the Niger Delta Area of Nigeria, Journal of Engineering Research in Africa, 1, 29-38.
[14] Igboanugo A. C. & M. K. Onifade (2011). Markov chain analysis of man power data of a Nigerian university, Journal of Innovative Research in Engineering and Science, 2, 107-123.
[15] Ramin, S. (2012). Forecasting time and place of earthquakes using a Semi-Markov model, Journal of Industrial Engineering International, 1-7.
[16] Shailesh, S. D. (2012). Credit card fraud detection using hidden Markov model. International Journal of Soft Computing and Engineering (IJSCE), 2, 2231-2307.
[17] Börcsök J. U. E., Machmur, D. (2007). Calculation of MTTF values with markov models for safety instrumented systems. Proceedings on a Conference on Applied Computer Science. Venice, Italy.
Cite This Article
  • APA Style

    Okwu Modestus Okechukwu, Thaddeus C. Nwaoha, Ombor Garrick. (2016). Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector. International Journal of Mechanical Engineering and Applications, 4(3), 103-108. https://doi.org/10.11648/j.ijmea.20160403.11

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    ACS Style

    Okwu Modestus Okechukwu; Thaddeus C. Nwaoha; Ombor Garrick. Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector. Int. J. Mech. Eng. Appl. 2016, 4(3), 103-108. doi: 10.11648/j.ijmea.20160403.11

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    AMA Style

    Okwu Modestus Okechukwu, Thaddeus C. Nwaoha, Ombor Garrick. Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector. Int J Mech Eng Appl. 2016;4(3):103-108. doi: 10.11648/j.ijmea.20160403.11

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  • @article{10.11648/j.ijmea.20160403.11,
      author = {Okwu Modestus Okechukwu and Thaddeus C. Nwaoha and Ombor Garrick},
      title = {Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector},
      journal = {International Journal of Mechanical Engineering and Applications},
      volume = {4},
      number = {3},
      pages = {103-108},
      doi = {10.11648/j.ijmea.20160403.11},
      url = {https://doi.org/10.11648/j.ijmea.20160403.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20160403.11},
      abstract = {In this research, Markov theoretical approach (MTA) was used to forecast the severity of risk workers were exposed to in the oil and gas industry and to determine the average period of time it would take workers to get exposed to menace of less severity and the possibility of transiting to a state where risk is high. The perils were classified into four states which include: catastrophic, critical, marginal and negligible. A solution procedure for addressing industrial hazards was developed from Markov. Fifty (50) workers in Warri Refining and Petrochemical Company (WRPC) were randomly selected for the purpose of questionnaire administration. Analysis of the data was carried out using QM software. The results showed that 56.66% of workers in marginal state would likely move to catastrophic state, while 43.34% of workers in marginal state would probably transit to critical state. Also 41.32% of workers in negligible state would move to a catastrophic state, while 58.68% of workers in negligible state would likely move to critical state within an average period of 2 to 3 years.  It is therefore recommended that provision of personal protective equipment and appropriate healthcare facilities be made, risk assessment of all workers be continuously carried out; workers must be properly trained on regular basis and the enforcement and strengthening of existing legislation effectively carried out to dispel these hazards.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Application of Markov Theoretical Model in Predicting Risk Severity and Exposure Levels of Workers in the Oil and Gas Sector
    AU  - Okwu Modestus Okechukwu
    AU  - Thaddeus C. Nwaoha
    AU  - Ombor Garrick
    Y1  - 2016/06/06
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijmea.20160403.11
    DO  - 10.11648/j.ijmea.20160403.11
    T2  - International Journal of Mechanical Engineering and Applications
    JF  - International Journal of Mechanical Engineering and Applications
    JO  - International Journal of Mechanical Engineering and Applications
    SP  - 103
    EP  - 108
    PB  - Science Publishing Group
    SN  - 2330-0248
    UR  - https://doi.org/10.11648/j.ijmea.20160403.11
    AB  - In this research, Markov theoretical approach (MTA) was used to forecast the severity of risk workers were exposed to in the oil and gas industry and to determine the average period of time it would take workers to get exposed to menace of less severity and the possibility of transiting to a state where risk is high. The perils were classified into four states which include: catastrophic, critical, marginal and negligible. A solution procedure for addressing industrial hazards was developed from Markov. Fifty (50) workers in Warri Refining and Petrochemical Company (WRPC) were randomly selected for the purpose of questionnaire administration. Analysis of the data was carried out using QM software. The results showed that 56.66% of workers in marginal state would likely move to catastrophic state, while 43.34% of workers in marginal state would probably transit to critical state. Also 41.32% of workers in negligible state would move to a catastrophic state, while 58.68% of workers in negligible state would likely move to critical state within an average period of 2 to 3 years.  It is therefore recommended that provision of personal protective equipment and appropriate healthcare facilities be made, risk assessment of all workers be continuously carried out; workers must be properly trained on regular basis and the enforcement and strengthening of existing legislation effectively carried out to dispel these hazards.
    VL  - 4
    IS  - 3
    ER  - 

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Author Information
  • Mechanical Engineering Department, Federal University of Petroleum Resources, Effurun, Delta State, Nigeria

  • Marine Engineering Department, Federal University Petroleum Resources, Effurun, Delta State, Nigeria

  • Marine Engineering Department, Federal University Petroleum Resources, Effurun, Delta State, Nigeria

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