The intelligent monitoring and evaluation of the psychophysiological state of the workers employed on offshore oil and gas platforms
Abstract
Oil and gas companies have an urgent need for technologies that provide complete and reliable information about the actual state of health and safety of personnel. To solve this problem, the article proposes a concept solution for the development of a system monitoring of the psychophysiological health of workers employed on offshore oil platforms. The concept is based on a person-centered approach and allows monitoring of health of employees simultaneously linking them to the context of the environment. The urgency of the problem is confirmed by statistical data, according to which workers in the oil and gas industry are 8 times more likely to get injured. The article analyzes the specific features of the professional activity of the workers employed on offshore oil platforms and shows that the deterioration of their health and psychological condition due to the long-term “sea environment” is unavoidable. It offers to develop an intelligent system for monitoring the psychophysiological condition of workers employed on offshore oil platforms and to assess its suitability for their position with the reference to the Cattell test and fuzzy patterns recognition. The development and systematic operation of such a system may timely detect undesirable consequences for the health status of workers employed on offshore oil platforms and prevent wrong decisions due to the “human factor”
Downloads
References
Mearns, K., Flin, R., Gordon, R., Fleming, M. (2001). Human and organizational factors in offshore safety. Work & Stress, 15 (2), 144–160. doi: https://doi.org/10.1080/026783701102678370110066616
Sector profiles. Oil and Gas. Available at: https://toolkit.cdcgroup.com/sector-profiles/oil-and-gas/
SOCAR reports (2011–2018). Socar. Available at: https://www.socar.az/socar/en/economics-and-statistics/economics-and-statistics/socar-reports
IoT solutions for upstream Oil and Gas. Available at: https://www.intel.com/content/dam/www/public/us/en/documents/solution-briefs/oil-and-gas-iot-brief.pdf
Grange, E. L. (2018). A Roadmap for Adopting a Digital Lifecycle Approach to Offshore Oil and Gas Production. Offshore Technology Conference. doi: https://doi.org/10.4043/28669-ms
Russell, McL. (2020). Types of Offshore Oil Rigs. Available at: https://www.treehugger.com/types-of-offshore-oil-rigs-4864111
Silva, V. L., Kovaleski, J. L., Pagani, R. N., Corsi, A., Gomes, M. A. S. (2020). Human Factor in Smart Industry: A Literature Review. Future Studies Research Journal: Trends and Strategies, 12 (1), 87–111. doi: https://doi.org/10.24023/futurejournal/2175-5825/2020.v12i1.473
Antonovsky, A., Pollock, C., Straker, L. (2013). Identification of the Human Factors Contributing to Maintenance Failures in a Petroleum Operation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56 (2), 306–321. doi: https://doi.org/10.1177/0018720813491424
Melikhov, A. N., Bernshtein, L. S., Korovin, S. Ya. (1990). Situational advising systems with fuzzy logic. Moscow: Nauka, 272.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning – I. Information Sciences, 8 (3), 199–249. doi: https://doi.org/10.1016/0020-0255(75)90036-5
Levin, V. I. (2001). A new generalization of operations on fuzzy sets. Journal of Computer and Systems Sciences International, 40 (1), 138–141. Available at: https://elibrary.ru/item.asp?id=14956908
Mammadova, M. H., Jabrayilova, Z. G. (2018). Fuzzy Multi-criteria Method to Support Group Decision Making in Human Resource Management. Studies in Fuzziness and Soft Computing, 209–222. doi: https://doi.org/10.1007/978-3-319-75408-6_17
Mammadova, M. H., Jabrayilova, Z. G. (2021). Conceptual approaches to intelligent human factor management on offshore oil and gas platforms. ARCTIC Journal, 74 (2), 19–40.
Copyright (c) 2021 Masuma Mammadovа, Zarifa Jabrayilova
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.
