iipp publishingJournal of Applied Engineering Science


DOI 10.5937/jaes17-22908
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License
Volume 17 article 643 pages: 541- 549

Vladimir Victorovich Bukhtoyarov* 
Siberian Federal University, Krasnoyarsk, Russia

Vadim Sergeevich Tynchenko 
Siberian Federal University, Krasnoyarsk, Russia

Eduard Arkadievich Petrovskiy 
Siberian Federal University, Krasnoyarsk, Russia

Fedor Anatolyevich Buryukin 
Siberian Federal University, Krasnoyarsk, Russia

The article deals with the problem of constructing models for automation of the technological situations recognition procedure during operation of oil wells. An approach was suggested to recognize technological situations associated with the operation of electrical centrifugal pumping units in oil production. The paper describes the methods for constructing models intended to recognize technological situations characterizing different types of failures of such electric centrifugal pumping (ECP) units. The models based on artificial neural networks, classification trees and support vector machines were considered as separate methods for constructing models for recognizing the technical state of ECP units in oil production. The paper presents the results of studying such methods in the tasks of assessing the technical state of several types of oil and gas production equipment. It is proposed to use sets of models enabling to integrate solutions of individual recognizers to improve situation recognition reliability. In the course of the research, tests were carried out on real operational data of ECP units. The research results showed that the use of such complex models will ensure a sufficiently high accuracy of recognition of technological situations. The proposed complex models provide higher stability of the results, which is confirmed by the results of statistical analysis of solutions obtained in the course of numerical experiments. Thus, it is shown that the proposed complex models for recognition of technological situations are an effective option to be used in object control systems during operation of oil producing wells.

View article

The work was completed with financial support from the Ministry of Education and Science of the Russian Federation within the framework of the Federal Target Program "Research and development in priority areas of scientific and technological complex of Russia for 2014 – 2020", action 1.3. Unique identifier of applied researches (project): RFMEFI57817X0236.

1. Bortnikov, A.E., Valeev, M.D. (2011). Operation of electrical centrifugal pump units in flooded wells. Oil Industry, vol. 8, 61-63.

2. Coltharp, E.D. (1984). Subsurface electrical centrifugal pumps. JPT, Journal of Petroleum Technology, vol. 36, no. 4, 645-652, DOI: 10.2118/9982-PA

3. Gabdullin, R.F. (2002). Operation of wells equipped with centrifugal electrical pumps units under complicated conditions. Oil Industry, vol. 4, 62.

4. Koshtorev, N.I., Zayakin, V.I. (2001). Electrocentrifugal pumps with an articulated joint for oil production. Chemical and Petroleum Engineering, vol. 37, no. 9-10, 516–517, DOI: 10.1023/A:1013354901763

5. Mdee, O.J., Joseph, K., Kimambo, C.Z., Nielsen, T.K. (2016). Reversing centrifugal pumps as alternative to conventional turbines for micro hydropower: A review. Proceedings of the 5th IASTED International Conference on Power and Energy Systems, p. 373-380.

6. Rodrigues, B.M., Cerqueira, A.A., Russo, C., Marques, M.R.C. (2010). Electroflocculation applied to the treatment of oil production wastewater. Periódico Tchê Química, vol. 7, no. 14, 7-15.

7. Cerqueira, A.A., Marques, M.R.C., Russo, C. (2010). Application of the technique of electroflocculation using alternate current in treatment of water production from oil industry. Periódico Tchê Química. vol. 7, no. 13, 33-45.

8. Zyatikov, P.N., Kozyrev, I.N., Deeva, V.S. (2016). Operation effectiveness of wells by enhancing the electric-centrifugal pump. IOP Conference Series: Earth and Environmental Science, vol. 43, no. 1, 012078, DOI: 10.1088/1755-1315/43/1/012078

9. Bukhtoyarov, V.V., Tynchenko, V.S., Petrovskiy, E.A., Tynchenko, V.V., Zhukov, V.G. (2018). Improvement of the methodology for determining reliability indicators of oil and gas equipment. International Review on Modelling and Simulations, vol. 11, no. 1, 37-50, DOI: 10.15866/iremos.v11i1.13994

10. Bukhtoyarov, V., Tynchenko, V., Petrovskiy, E., Bukhtoyarova, N., & Zhukov, V. [2018]. investigation of methods for modeling petroleum refining facilities to improve the reliability of predictive decision models. Journal of Applied Engineering Science, 16(2), 246-253.

11. Bukharov, O.E., Bogolyubov, D.P. (2015). Development of a decision support system based on neural networks and a genetic algorithm. Expert Systems with Applications, vol. 42, no. 15-16, 6177-6183, DOI: 10.1016/j.eswa.2015.03.018

12. Wu, X., Sun, H., Wu, Z., Miao, X. (2017). Modeling and evaluating of decision support system based on cost-sensitive multiclass classification algorithms. Advances in Intelligent Systems and Computing, vol. 541, 433-438.

13. Akdeniz, E., Bagriyanik, M. (2016). A knowledge based decision support algorithm for power transmission system vulnerability impact reduction. International Journal of Electrical Power and Energy Systems, vol. 78, 436-444, DOI: 10.1016/j.ijepes.2015.11.041

14. Alencar, T.R., Gramulia, J., Otobe, R.F., Asano, P.T.L. (2015). Decision support system based on genetic algorithms for optimizing the operation planning of hydrothermal power systems. 5th International Youth Conference on Energy, 7180815.

15. Yun, T., Yi, G.-S. (2008). Application of random forest algorithm for the decision support system of medical diagnosis with the selection of significant clinical test. Transactions of the Korean Institute of Electrical Engineers, vol. 57, no. 6, 1058-1062.

16. Yan, H., Ding, X., Peng, C., Xiao, S. (2004). Study on medical diagnosis decision support system for heart diseases based on hybrid genetic algorithm. Journal of Biomedical Engineering, vol. 21, no. 2, 302-305.

17. Yang, J., Wang, X., Dang, J. (2014). On the algorithm of the medical diagnostic decision support system under the mobile platform. Open Electrical and Electronic Engineering Journal, vol. 8, no. 1, 589-593.

18. Capkovič, F. (1988). A decision support algorithm for flexible manufacturing systems control. Computers in Industry, vol. 10, no. 3, 165-170, DOI: 10.1016/0166-3615(88)90035-8

19. Murygin, A.V., Tynchenko, V.S., Laptenok, V.D., Emilova, O.A., Bocharov, A.N. (2017). Complex of automated equipment and technologies for waveguides soldering using induction heating. IOP Conference Series: Materials Science and Engineering, vol. 173, no. 1, 012023, DOI: 10.1088/1757-899x/173/1/012023

20. Lachhab, M., Béler, C., Coudert, T. (2018). A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm. Engineering Applications of Artificial Intelligence, vol. 72, 310-326, DOI: 10.1016/j.engappai.2018.04.001

21. Jun Tan, C., Hanoun, S., Peng Lim, C. (2015). A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing. 9th Annual IEEE International Systems Conference, p. 170-174.

22. Zimmermann, H.-J., Sebastian, H.-J. (1995). Intelligent system design support by fuzzy-multi-criteria decision making and/or evolutionary algorithms. IEEE International Conference on Fuzzy Systems, vol. 1, p. 367-374.

23. Milov, A.V., Tynchenko, V.S., Petrenko, V.E. (2019). Algorithmic and software to identify errors in measuring equipment during the formation of permanent joints. 2018 International Multi-Conference on Industrial Engineering and Modern Technologies, 8602515.

24. Peng, Y., Yang, X., Xu, W. (2018). Optimization research of decision support system based on data mining algorithm. Wireless Personal Communications, vol. 102, no. 4, 2913-2925, DOI: 10.1007/s11277-018-5315-3

25. Tynchenko, V.S., Tynchenko, V.V., Bukhtoyarov, V.V., Tynchenko, S.V., Petrovskyi, E.A. (2016). The multi-objective optimization of complex objects neural network models. Indian Journal of Science and Technology, vol. 9, no. 29, 99467, DOI: 10.17485/ijst/2016/v9i29/99467

26. Tynchenko, V.S., Petrovsky, E.A., Tynchenko, V.V. (2016). The parallel genetic algorithm for construction of technological objects neural network models. 2nd International Conference on Industrial Engineering, Applications and Manufacturing, 7911573.

27. Glorot, X., Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of Machine Learning Research, no. 9, 249-256.

28. Ali, S.H., Ali, A.H. (2019). Crude oil price prediction based on soft computing model: Case study of Iraq. Journal of Southwest Jiaotong University, vol. 54, no. 4, DOI: 10.35741/issn.0258-2724.54.4.36

29. Mahmood, M., Al-Kubaisy, W.J., Al-Khateeb, B. (2019). Using artificial neural network for multimedia information retrieval. Journal of Southwest Jiaotong University, vol. 54, no. 3, DOI: 10.35741/issn.0258-2724.54.3.19

30. Zhang, G., Patuwo, B.E., Hu, M.Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, vol. 14, no. 1, 35-62, DOI: 10.1016/S0169-2070(97)00044-7

31. Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, vol. 1, no. 1, 81-106, DOI: 10.1023/A:1022643204877

32. Geurts, P., Ernst, D., Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, vol. 63, no. 1, 3-42, DOI: 10.1007/s10994-006-6226-1

33. Quinlan, J.R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies, vol. 27, no. 3, 221-234, DOI: 10.1016/S0020-7373(87)80053-6

34. Boser, B.E., Guyon, I.M., Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, p. 144-152.

35. Hsu, C.-W., Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, vol. 13, no. 2, 415-425, DOI: 10.1109/72.991427