Advertisement

Decision-Making on Restriction of Water Inflows into Oil Wells in Dependence on the Type of Initial Information

  • B. N. Koilybayev
  • A. S. Strekov
  • K. T. Bissembayeva
  • P. Z. Mammadov
  • D. A. Akhmetov
  • O. G. Kirisenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

The report is devoted to the problem of limiting water inflows into oil wells. A large number of studies have been devoted to this subject, theoretical and experimental work has been carried out, but the inadequacy of the decision-making process on the choice of the bottomhole zone treatment method significantly reduces the effectiveness of the problem solution. Depending on the type of initial information, various methods were used at different times, in particular statistical methods known from the theory of fuzzy sets and others. Investigations and analysis of the processes of water inflows show that a number of factors make an influence on efficiency of this process. In this regard, in this paper, the collected data about influence of the of various factors on effectiveness are subjected to fuzzy cluster analysis with the establishment of fuzzy rules that express the influence of the selected factors on the effect duration and the amount of additional extracted oil. Four clusters were obtained with the further formulation of fuzzy rules according to the principle “if …, then …” as a result of the application of fuzzy cluster-analysis program.

Keywords

Well Water inflow Bottomhole zone Fuzzy sets Fuzzy cluster-analysis Oil recovery Permeability Well rate Polymer solution 

References

  1. 1.
    Abasov, M.T., Djafarova, N.M., Strekov, A.S., Efendiyev, G.M., Manafov, G.R.: Prediction of time of water inflows restriction with polymer solutions in producing wells. In: Proceedings of the Azerbaijan National Academy of Sciences, The Sciences of Earth, №. 2, pp. 97–102 (2001)Google Scholar
  2. 2.
    Bezdek, J.C., Ehrlich, R., Full, W.E.: The fuzzy C-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)CrossRefGoogle Scholar
  3. 3.
    Aliev, R.A., Guirimov, B.G.: Type-2 Fuzzy Neural Networks and Their Applications. Springer, Switzerland (2014). http://www.springer.com/us/book/9783319090719Google Scholar
  4. 4.
    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, Izmir, Turkey, vol. 2013, p. 21 (2013)Google Scholar
  5. 5.
    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, 29–30 August 2016, Vienna, Austria (2016). Procedia Comput. Sci. 102, 159–162 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • B. N. Koilybayev
    • 1
  • A. S. Strekov
    • 2
  • K. T. Bissembayeva
    • 1
  • P. Z. Mammadov
    • 3
  • D. A. Akhmetov
    • 1
  • O. G. Kirisenko
    • 2
  1. 1.Caspian State University of Technology and Engineering named after Sh. YessenovAktauRepublic of Kazakhstan
  2. 2.Oil and Gas Institute, Azerbaijan National Academy of SciencesBakuAzerbaijan
  3. 3.Azerbaijan State Oil and Industry UniversityBakuAzerbaijan

Personalised recommendations