Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-38729 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1052 pages: 76-86

Ekaterina Kusimova*
Saint Petersburg Mining University; 2, 21st Line, St Petersburg

Liliya Saychenko
Saint Petersburg Mining University; 2, 21st Line, St Petersburg

Nelli Islamova
Gazpromneft STC

Pavel Drofa
Gazpromneft STC

Elena Safiullina
Saint Petersburg Mining University; 2, 21st Line, St Petersburg

Alexey Dengaev
National University of Oil and Gas «Gubkin University»; 65 Leninsky Prospekt, Moscow

In the process of field exploration, along with regular flooding, a significant part of the wells is flooded prematurely due to leakage of the string and outer annulus. In an effort to intensify the flow of oil to the bottom of wells in field conditions, specialists often try to solve this problem by using various technologies that change the reservoir characteristics of the formation. Any increase in pressure that exceeds the strength of the rocks in compression or tension leads to rock deformation (destruction of the cement stone, creation of new cracks). Furthermore, repeated operations under pressure, as a rule, lead to an increase in water cut and the appearance of behind-the-casing circulations. For that reason, an important condition for maintaining their efficient operation is the timely forecasting of such negative phenomena as behind-casing cross flow and casing leakage. The purpose of the work is to increase the efficiency of well interventions and workover operations by using machine learning algorithms for predicting well disturbances. Prediction based on machine learning methods, regression analysis, identifying outliers in the data, visualization and interactive processing. The algorithms based on oil wells operation data allow training the forecasting model and, on its basis, determine the presence or absence of disturbances in the wells. As a result, the machine forecast showed high accuracy in identifying wells with disturbances. Based on this, candidate wells can be selected for further work. For each specific well, an optimal set of studies can be planned, as well as candidate wells can be selected for further repair and isolation work. In addition, in the course of this work, a set of scientific and technical solutions was developed using machine learning algorithms. This approach will allow predicting disturbances in the well without stopping it.

View article

1.      Mittal, A., Slaughter, A. and Bansa, V., 2017. From bytes to barrels. The digital transformation in upstream oil and gas. Deloitte Insights. Available at:

2.      [Accessed 17 September 2021].

3.      Baker, D., 2021. Digital Technology Trends in the Oil and Gas Industry. GEO ExPro. Available at: [Accessed 19 September 2021].

4.      Legkokonets, V.A., Islamov, S.R., Mardashov, D.V., 2019. Multifactor analysis of well killing operations on oil and gas condensate field with a fractured reservoir. Proceedings of the International Forum-Contest of Young Researchers: Topical Issues of Rational Use of Mineral Resources, Taylor & Francis: London, UK, pp. 111-118.

5.      Stroykov, G., Babyr, N., Ilin, I., Marchenko, R., 2021. System of Comprehensive Assessment of Project Risks in the Energy Industry, International Journal of Engineering, 34(7), pp. 1778-1784. DOI: 10.5829/ije.2021.34.07a.22.

6.      Slaughter A., Bean G., andMittal A., August 14, 2015. Connected barrels: Transforming oil and gas strategies with the Internet of Things, Deloitte University Press. Available at: [Accessed 10 September 2021].

7.      Alsaihati, A., Elkatatny, S., Mahmoud, A. and Abdulraheem, A., 2021. Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study. Journal of Energy Resources Technology, 143(4). DOI:10.1115/1.4048070.

8.      Bello, O., Holzmann, J.,Yaqoob, T. and Teodoriu, C., 2015. Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art. Journal of Artificial Intelligence and Soft Computing Research, Vol.5 (Issue 2), pp. 121-139. DOI: 10.1515/jaiscr-2015-0024.

9.      Forsyth, D., 2020. Applied machine learning. [s.l.]: Springer Nature.

10.   Babyr, N., & Babyr, K., 2021. To improve the contact adaptability of mechanical roof support. Paper presented at the E3S Web of Conferences, 266. DOI:10.1051/e3sconf/202126603015.

11.   Turner, R., 2019. Machine Learning: The Ultimate Beginner's Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by Step. N.p., nelly B.L. International Consulting Limited.

12. 2021. Geological prospecting. Available at: [Accessed 2 October 2021].

13. 2021. Gazprom Neft and IBM Research Brazil work on geological processing AI. Available at: [Accessed 2 October 2021].

14. 2021. Gazprom Neft and Nefteservisholding to cooperate in technological development - Investors - «Gazprom Neft» PJSC. Available at: [Accessed 13 September 2021].

15.   Martyushev, D., Ponomareva, I., Zakharov, L. and Shadrov, T., 2021. Application Of Machine Learning For Forecasting Formation Pressure In Oil Field Development. Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov, 332(10), pp.140-149. DOI: 10.18799/24131830/2021/10/3401.

16.    Parviainen, P., Kaariainen, J., Tihinen, M. &Teppola, S., 2017. Tackling the digitalization challenge: how to benefit from digitalization in practice. International Journal of Information Systems and Project Management,5(1), pp. 63-77. DOI: 10.12821/ijispm050104.

17.   Wei, Z., Zhu, S., Dai, X., Wang, X., Yapanto, L. M., & Raupov, I. R., 2021. Multi-criteria decision making approaches to select appropriate enhanced oil recovery techniques in petroleum industries. Energy Reports, 7(3), pp. 2751-2758. DOI: 10.1016/j.egyr.2021.05.002.

18.   Rogatchev, M. K., & Kuznetsova, A. N., 2019. Technology of low-permeable polimictic reservoirs water-flooding with surfactant solutions. Paper presented at the Innovation-Based Development of the Mineral Resources Sector: Challenges and Prospects - 11th Conference of the Russian-German Raw Materials, 2018, 161-166.

19.   Temizel, C., Canbaz, C., Palabiyik, Y., Putra, D., Asena, A., Ranjith, R. and Jongkittinarukorn, K., 2019. A Comprehensive Review of Smart/Intelligent Oilfield Technologies and Applications in the Oil and Gas Industry. SPE Middle East Oil and Gas Show and Conference. DOI: 10.2118/195095-MS.

20.   Bourgeois, B. et al., 2015. A Framework for Sustainable Digital Oilfield Solutions. SPE Digital Energy Conference and Exhibition. The Woodlands, Texas, USA, Society of Petroleum Engineers. DOI: 10.2118/173436-MS.

21.   Choobbasti A. J., Farrokhzad F., Mashaie E.R., Azar P.H. Mapping of soil layers using artificial neural network (case study of Babol, northern Iran). Journal of the South African Institution of Civil Engineering, March 2015, vol. 57, no. 1, pp. 59-66.

22.   Rodina S. N., Silkin K. Yu. Primenenie nejrosetevogo podhoda pri interpretacii karotazhnyh dannyh [Application of neural network approach in interpreting well logging data]. Vestnik VGU, Geologiya [Proceedings of Voronezh State University. Geology], 2007, no. 2, pp. 184-188.

23.   Kuroda M. C., Vidal A. C., Almeida de Carvalho A. M. Interpretation of seismic multiattributes using a neural network. Journal of Applied Geophysics, 2012, vol. 85, pp. 15-24.

24.   Akinyokun O.C., Enikanselu P.A., Adeyemo A.B., Adesida A. Well log interpretation model for the determination of lithology and fluid contents. The Pacific Journal of Science and Technology, 2009, vol. 10, no. 1, pp. 507-517.

25.   Shajbakov R. A. Ispol’zovanie nejrosetevogo apparata dlya identifikacii granic geologicheskih ob”ektov [The use of neural network mechanism for identifying of geological objects boundaries]. Materials of the international scientific conference “Tekhnicheskie nauki: tradicii i innovacii” [“Technical Sciences: Traditions and Innovations”], Chelyabinsk: Dva komsomol’ca, 2012, pp. 8-11.

26.   Nakutnyy P., Asghari K., Torn A. Analysis of waterflooding through application of neural networks. Conference Paper, Canadian International Petroleum Conference, Calgary, Alberta, 17-19 June, 2008.

27.   Huang Z., Shimeld J., Williamson M., Katsube J. Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada, Geophysics, 1996, vol. 61, pp. 422-436.

28.   Khormali A., Moghadasi R., Kazemzadeh Y., Struchkov I., 2021. Development of a new chemical solvent package for increasing the asphaltene removal performance under static and dynamic conditions, Journal of Petroleum Science and Engineering, vol. 206, DOI: 10.1016/j.petrol.2021.109066.

29.   Borisov A.S., Kulikov S.A. Iskusstvennye nejronnye seti v prognozirovanii neftegazonosnosti po dannym sejsmorazvedki [Artificial neural networks in the forecasting of petroleum potential from seismic data]. Kazanskij (Privolzhskij) federal’nyj universitet, Institut geologii i neftegazovyh tekhnologij [Kazan Federal University, Institute of Geology and Petroleum Technologies]. 2012.

30.   Mutalimov, V., Kovaleva, I., Mikhaylov, A., & Stepanova, D. (2021). Assessing regional growth of small business in Russia. Entrepreneurial Business and Economics Review, 9(3), 119-133.

31.   Kaznacheev P. F., Samoylova R. V., Kurchiski N. V. Application of artificial intelligence methods to improve efficiency in the oil and gas and other raw materials industries. Economic Policy, 2016, vol. 11, no. 5, pp. 188-197.

32.   Sultanbekov, R., Beloglazov, I.; Islamov, S., Ong, M.C., 2021. Exploring of the incompatibility of marine residual fuel: A case study using machine learning methods. Energies 2021, 14, 8422. (20)

33.   An, J., Mikhaylov, A., Jung, S.-U. (2020). The Strategy of South Korea in the Global Oil Market. Energies, 13(10), 2491.

34.   Mardashov D. V., 2006. Improving the technology of casing the near-wellbore zone in order to eliminate crossflows in gas condensate wells. Journal of Mining Institute, 167(2), p. 35. (21)

35.   Nikolaev N. I., & Khaoya L., 2017. Results of cement-to-rock contact study. Journal of Mining Institute, 226, 428.

36.   Syah, R., Alizadeh, S. M., Nurgalieva, K. S., Guerrero, J. W. G., Nasution, M. K. M., Davarpanah, A., Metwally, A. S. M., 2021. A laboratory approach to measure enhanced gas recovery from a tight gas reservoir during supercritical carbon dioxide injection. Sustainability (Switzerland), 13(21). DOI:10.3390/su132111606.

37.   Dvoynikov M. V., Kuchin V. N., & Mintzaev M. S., 2021. Development of viscoelastic systems and technologies for isolating water-bearing horizons with abnormal formation pressures during oil and gas wells drilling. Journal of Mining Institute, 247(1), 1-9.

38.   Buza, K., 2018. Time Series Classification and its Applications. Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. DOI: 10.1145/3227609.3227690.

39.   Islamov, S., Grigoriev, A., Beloglazov, I., Savchenkov, S., Gudmestad, O.T., 2021. Research risk factors in monitoring well drilling – A case study using machine learning methods. Symmetry 2021, 13, 1293.

40.   Kirasich, K., Smith, T., and Sadler, B., 2018. Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review: Vol. 1: No. 3, Article 9. Available at: [Accessed 29 October 2021].

41.   Brownlee, J., 2016. Machine Learning Algorithms From Scratch with Python. Machine Learning Mastery.

42.   Raupov, I. and Milic J., 2022. Improvement of operational efficiency of high water-cut oil wells. IOP Conf. Ser.: Earth Environ. Sci. 1021 012077

43.   Tetko, I., Kurkova, V., Karpov, P. and Theis, F., 2019. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV. Springer International Publishing.