Classification of Hard-to-Recover Hydrocarbon Reserves of Kazakhstan with the Use of Fuzzy Cluster-Analysis

  • D. A. Akhmetov
  • G. M. EfendiyevEmail author
  • M. K. Karazhanova
  • B. N. Koylibaev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


This report is devoted to the classification of hard-to-recover oil reserves. The analysis of existing classifications has been carried out preliminary and the necessity of using a method that takes into account the whole range of characteristics allowing to classify oil and conditions of occurrence to a particular class has been shown. In this connection, we applied the method of fuzzy cluster analysis. The tasks of cluster-analysis have been widely used in economics, sociology, medicine, geology, oilfield practice and other industries, i.e. wherever there are sets of objects of an arbitrary nature, described in the form of vectors x = {x1, x2, …, xN}, which must be automatically divided into groups of homogeneous objects according to the similarity within the homogeneous object (cluster) and the difference between these objects. A considerable amount of literature has accumulated in this direction. As noted in the literature, there are more than one hundred different clustering algorithms, among them hierarchical and non-hierarchical cluster-analyzes, fuzzy clustering.

In order to classify hard-to-recover reserves, we performed clustering using the fuzzy cluster-analysis algorithm. For this purpose, data were collected on the viscosity, oil density and permeability of oil conditions from the oilfields of Kazakhstan. As a result, 4 classes were obtained, each of which characterizes the difficulty of extracting oil: the layer is permeable, highly viscous and very heavy oil; medium permeability layer, viscous and heavy oil; high-permeability reservoir, medium viscosity oil and medium-density oil; low-permeability reservoir, low viscosity oil, light oil.


Permeability Density Viscosity Hard-to-recover reserves Fuzzy clustering 


  1. 1.
    Nikolin, I.V.: Methods for the development of heavy oils and natural bitumen. In: Science – The Foundation for Solving the Technological Problems of Development of Russia, vol. 2, pp. 54–68 (2007)Google Scholar
  2. 2.
    Company News. In: Oil Industry, vol. 1, p. 9 (2009)Google Scholar
  3. 3.
    Vakhitov, G.G., Morozov, V.D., Safiullin, R.K.H.: Problems of well development of high-viscosity oil and natural bitumen deposits abroad. In: Review, Inform. “Oilfield Business”, vol. 19, no. 126, p. 49 (1986)Google Scholar
  4. 4.
    Verevkin, K.I., Diyashev, R.N.: Classification of hydrocarbons in the selection of methods for their extraction. In: Oil Industry, vol. 3, pp. 31–34 (1982)Google Scholar
  5. 5.
    Skorovarov, Y.N., Trebin, G.F., Kapyryp, Y.V.: Properties of high-viscosity oil deposits of the USSR. In: Geology of Oil and Gas, vol. 2, pp. 24–27 (1985)Google Scholar
  6. 6.
    Halimov, E.M., Klimushin, I.M., Ferdman, L.N.: Geology of high-viscosity oil deposits of the USSR. Nedra, Reference Book, 174 p. (1987)Google Scholar
  7. 7.
    Steam-thermal effect on the reservoir.
  8. 8.
    Makarevich, V.N., Iskritskaya, N.I., Bogoslovsky, S.A.: Resource potential of heavy oil deposits in the European part of the Russian Federation. In: Oil and Gas Geology. Theory and Practice, vol. 3, p. 7 (2012).
  9. 9.
    Nugiev, M.A.: About none-quilibrium reological properties of high-viscosity oils of some deposits of Western Kazakhstan.,
  10. 10.
    Yashchenko, I., Polishchuk, Y., Kozin, E.: Hard-to-recover oil: classification and analysis of qualitative features. Oil & Gas J. Russia. Geol. Geophys. 11, 64–70 (2015)Google Scholar
  11. 11.
    Lisovsky, N.N., Halimov, E.M.: On the classification of hard-to-recover reserves. In: Nedra, Vestnik, vol. 6, pp. 33–35 (2009)Google Scholar
  12. 12.
    Purtova, I.P., Varichenko, A.I., Shpurov, I.V.: Hard-to-recover oil reserves. Terminology. Problems and state of development in Russia. In: Science and Fuel and Energy, vol. 6, pp. 21–26 (2011)Google Scholar
  13. 13.
    Polishchuk, Y.M., Yashchenko, I.G.: Comparative analysis of the quality of Russian oil. In: Technologies TEK, vol. 3, pp. 51–56 (2003)Google Scholar
  14. 14.
    Yashchenko, I.G., Polishchuk, Y.M.: Comparative analysis of the quality of hard-to-recover oils. In: The Gas Industry, vol. 5, no. 722, pp. 18–23 (2015)Google Scholar
  15. 15.
  16. 16.
    Maksutov, R., Orlov, G., Osipov, A.: Development of high-viscosity oil reserves in Russia. In: Technologies of the Fuel and Energy Sector, vol. 6, pp. 36–40 (2005)Google Scholar
  17. 17.
    Yashchenko, I.G., Polishchuk, Y.M.: Hard-to-recover oil: physical and chemical properties and regularities of placement. In: Novikov, A.A. (ed.) Tomsk, B-Spectrum, p. 154 (2014)Google Scholar
  18. 18.
    Shpurov, I.V., Rastrogin, A.E., Bratkova, V.G.: On the problem of development of hard-to-recover oil reserves in Western Siberia. In: Oil Industry, vol. 12, pp. 95–97 (2014)Google Scholar
  19. 19.
    Degtyarev, V.N.: About the bank of quality of oil. In: Oil Industry, vol. 3, pp. 62–63 (1997)Google Scholar
  20. 20.
    Polishchuk, Y.M., Yashchenko, I.G.: Analysis of eurasian oils quality. In: Oil industry, vol. 1, pp. 66–68 (2002)Google Scholar
  21. 21.
    Kritskaya, E.B., Chizh, D.V.: Study of changes in the physico-chemical parameters of the Ciscaucasian oil. In: Voronezh State University. Series: Chemistry. Biology. Pharmacy. Vestnik, vol. 1, pp. 21–23 (2013)Google Scholar
  22. 22.
    Klubkov, S.: Stimulation of TRIZ development will help support the level of oil production in Russia. Oil & Gas J. Russia 7(95), 6–11 (2015)Google Scholar
  23. 23.
    Raupov, I.R., Kondrasheva, N.K., Burkhanov, R.N.: Development of a mobile device for measuring the optical properties of oil in solving geological and fishing problems. Electron. Sci. J. Oil Gas Bus. 3, 17–32 (2014).
  24. 24.
    Kluvert, N.-B., Savenok, O.V.: Hard-to-recover hydrocarbon reserves, important resources in the territory of the Federal Republic of Nigeria. In: The Twenty-First International Scientific and Practical Conference, Current State of Natural and Technical Sciences, Moscow, 46 (2015).
  25. 25.
    Krylov, V.Y., Ostryakova, T.V.: Mathematical methods of data processing in psychological studies: new methods of cluster-analysis based on the psychological theory of L.S. Vygotsky’s concept development. Psychol. J. 1, 16 (1995)Google Scholar
  26. 26.
    Savchenko, T.N.: The application of methods of cluster-analysis for the processing of data of psychological research. In: Experimental Psychology, vol. 2 (2010)Google Scholar
  27. 27.
    Bezdek, J.C., Ehrlich, R., Full, W.: The fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)Google Scholar
  28. 28.
    Aliev, R.A., Guirimov, B.G.: Type-2 Fuzzy Neural Networks and Their Applications.
  29. 29.
    Turksen, I.B.: Full Type 2 to Type n Fuzzy System Models. In: Seventh International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control. Turkey, Izmir, p. 21 (2013)Google Scholar
  30. 30.
    Efendiyev, G.M., Mammadov, P.Z., Piriverdiyev, I.A., Mammadov, V.N.: Clustering of geological objects using FCM-algorithm and evaluation of the rate of lost circulation. In: 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, Vienna, Austria, 29–30 August. Procedia Comput. Sci. 102, 159–162 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • D. A. Akhmetov
    • 1
  • G. M. Efendiyev
    • 2
    Email author
  • M. K. Karazhanova
    • 1
  • B. N. Koylibaev
    • 1
  1. 1.Yessenov UniversityAktau, Mangistau RegionKazakhstan
  2. 2.Institute of Oil and GasBakuAzerbaijan

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