Application of mathematical modelling methods in oil production management

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Authors:


B.Orazbayev, orcid.org/0000-0001-5424-0315, L.N.Gumilyov Eurasian National University, Nur-Sultan, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Issa, orcid.org/0000-0002-4849-9408, L.N.Gumilyov Eurasian National University, Nur-Sultan, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.Iskakova, orcid.org/0000-0002-6987-4772, Atyrau Oil and Gas University named after S.Utebayev, Atyrau, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

L.Kurmangaziyeva, orcid.org/0000-0003-0640-7306, Kh. Dosmukhamedov Atyrau University, Atyrau, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (4): 112 - 116

https://doi.org/10.33271/nvngu/2022-4/112



Abstract:



Purpose.
To assess the effectiveness of the practical application of mathematical modelling methods of the operation principles of oil production management systems in a fuzzy environment and the main aspects of their application in oil production.


Methodology.
The basis of the methodological approach in this study is a combination of methods of quantitative and qualitative analysis of the basic principles of the development of mathematical models for managing oil production processes in a fuzzy environment.


Findings.
The authors investigated the available methods of mathematical modeling in order to select the optimal possibilities for creating mathematical models. A qualitative assessment of the effectiveness of the practical application of methods of mathematical modeling of the principles of operation of oil production management systems in a fuzzy environment was formulated. The main aspects of application of methods of mathematical modeling directly in the process of oil production were established.


Originality.
A fuzzy approach is proposed for solving the problem of multi-criteria optimization in the development of a field and oil production, in which the task is set and solved in a fuzzy environment without previously converting them to equivalent clear tasks. This ensures the correctness and efficiency of the solution by increasing the adequacy of the description of the problem in a fuzzy environment.


Practical value.
The results obtained and the conclusions formulated on their basis are of considerable practical importance for employees of research institutes of the oil industry responsible for the development of effective methods of mathematical modelling of oil production process control systems and for employees of oil companies whose professional duties include the introduction of the mentioned mathematical models in oil fields.



Keywords:
mathematical models, oil industry, oil production management

References.


1. Gao, Q., & Karimi, H.R. (2019). Stability, control and application of time-delay systems. London: Academic Press. ISBN: 9780128149294.

2. Singh, H., Srivstana, H., & Baleanu, D. (2021). Methods of mathematical modelling. London: Academic Press. ISBN: 9780367776558.

3. Kumar, A., & Ram, M. (2021). The handbook of reliability, maintenance, and system safety through mathematical modeling. Oxford: Elsevier. ISBN: 9780128195826.

4. Jones, J., & Stanley, E. (2016). Stream ecosystems in a changing environment. London: Academic Press. ISBN: 9780124058903.

5. Delavar, M., & Wang, J. (2021). Advanced mathematical modelling of biofilms and its applications. London: Academic Press. ISBN: 0323903746.

6. Davim, J.P. (2017). Computational methods and production engineering. Oxford: Woodhead Publishing. ISBN:9780857094827.

7. Sayyaadi, H. (2020). Modeling, assessment, and optimization of energy systems. London: Academic Press. ISBN:9780128166574.

8. Radwan, A., Khanday, F., & Said, L. (2021). Fractional-order modeling of dynamic systems with applications in optimization, signal processing, and control. Oxford: Elsevier. ISBN: 9780323902038.

9. Azar, A.T., Radwan, A., & Vaidyanathan, S. (2018). Mathematical techniques of fractional order systems. Oxford: Elsevier. ISBN:9780128135938.

10. Chen, Y., Ran, Y., Wang, Z., Li, X., Yang, X., & Zhang, G. (2020). An extended multimoora method based on owga operator and choquet integral for risk prioritization identification of failure modes. Engineering Applications of Artificial Intelligence, 91, article number 103605. https://doi.org/10.1016/j.engappai.2020.103605.

11. Almeida, C.G., Bertone, A.M.A., & Jafelice, R.M. (2018). Fuzzification of the miscible displacement model in heterogeneous porous media. Journal of Mathematical Analysis and Applications, 463(1), 242-267. https://doi.org/10.1016/j.jmaa.2018.03.015.

12. Liao, H., & Deng, Q. (2018). EES-EOQ model with uncertain acquisition quantity and market demand in dedicated or combined remanufacturing systems. Applied Mathematical Modelling, 64, 135-167. https://doi.org/10.1016/j.apm.2018.07.026.

13. Liu, H-C., You, J-X., Li, Z.W., & Tian, G. (2017). Fuzzy Petri Nets for knowledge representation and reasoning: a literature review. Engineering Applications of Artificial Intelligence, 60, 45-56. https://doi.org/10.1016/j.engappai.2017.01.012.

14. Doan, L.T.T., Amer, Y., Lee, S-H., Phuc, P.N.K., & Dat, L.Q. (2019). A comprehensive reverse supply chain model using an interactive fuzzy approach a case study on the vietnamese electronics industry. Applied Mathematical Modelling, 76, 87-108. https://doi.org/10.1016/j.apm.2019.06.003.

15. Duan, C., Li, Y., Pu, H., & Luo, J. (2021). Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring. Applied Mathematical Modelling, 103, 388-408. https://doi.org/10.1016/j.apm.2021.10.015.

16. Mikuckas, A., Ciuras, D., Prasauskas, T., Mikuckiene, I., Lukas,R., Kazanavicius, E., Jurelionis, A., & Martuzevicius, D. (2017). A grey model approach to indoor air quality management in rooms based on real-time sensing of particles and volatile organic compounds. Applied Mathematical Modelling, 42, 290-299. https://doi.org/10.1016/j.apm.2016.10.030.

17. Mittal, K., Jain, A., Vaisla, K.S., Castillo, O., & Kacprzyk, J. (2020). A comprehensive review on type 2 fuzzy logic applications: past, present and future. Engineering Applications of Artificial Intelligence, 95, article number 103916. https://doi.org/10.1016/j.engappai.2020.103916.

18. Lu, T., & Liu, S-T. (2018). Fuzzy nonlinear programming approach to the evaluation of manufacturing processes. Engineering Applications of Artificial Intelligence, 72, 183-189. https://doi.org/10.1016/j.engappai.2018.04.003.

 

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ISSN (print) 2071-2227,
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Journal was registered by Ministry of Justice of Ukraine.
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