Non-parametric Criteria of Chaotic Data Analysis in Oil Production

  • T. Sh. Salavatov
  • A. A. Abbasov
  • H. Kh. Malikov
  • D. F. Guseynova
  • A. A. SuleymanovEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


This paper presents distribution analysis of oil production data chaotic fluctuations. Use of distribution analysis allows giving numerical characterization to fluctuation processes. This enables prediction of certain problems in well life based on the change of this numerical characterization.

In particular, water break-through prediction is considered in this paper with application of distribution analysis.

The paper suggests non-parametric criteria for analysis of production data chaotic fluctuations.

The suggested methods enable analysis changing of technological process with data distribution skewness, and also if using of other method is not proper or not to purpose.

The offered non-parametric method criteria enable simplifying of processes’ analysis, which are characterized by multi-fractal, chaotic data, and their evaluation procedure can be simply implemented.

Validity of diagnosis methods has been confirmed in modeling and practical examples.


Chaotic fluctuations Distribution Non-parametric criteria 


  1. 1.
    Haken, H.: Synergetics: Introduction and Advanced Topics. Springer, Berlin (2004)CrossRefGoogle Scholar
  2. 2.
    Feder, E.: Fractals. Plenum Press, New York (1988)CrossRefGoogle Scholar
  3. 3.
    Mirzajanzadeh, A., Aliev, N., Yusifzade, K.: Fragments on Development of Offshore Oil and Gas Fields. Elm, Baku (1997)Google Scholar
  4. 4.
    Mirzajanzadeh, A., Hasanov, M., Bahtizin, R.: Modeling of Oil and Gas Production Processes. ICR, Moscow (2004)Google Scholar
  5. 5.
    Bendat, J., Piersol, A.: Random Data: Analysis and Measurements Procedures. Wiley, New York (1971)zbMATHGoogle Scholar
  6. 6.
    Mirzajanzadeh, A., Sultanov, Ch.: Reservoir Oil Recovery Process Diacoptics. APC, Baku (1995)Google Scholar
  7. 7.
    Jensen, J., Lake, L., Corbett, P., Goggin, D.: Statistics for Petroleum Engineers and Geoscientists. Elsevier, Amsterdam (2000)Google Scholar
  8. 8.
    Mandelbrot, B.: Fractals, hasard et finance, 246 p. Flammarion, Paris (1997)Google Scholar
  9. 9.
    Belfield, W.C.: Incorporating spatial distribution into stochastic modeling of fractures: multifractals and levy-stable statistics. J. Struct. Geol. 20(4), 473–486 (1998)CrossRefGoogle Scholar
  10. 10.
    Aguilera, R.F., Ramirez, J.F., Ortega, C., Aguilera, R.: A variable shape distribution model for characterization of pore throat radii, drill cuttings, fracture apertures and petrophysical properties in tight, shale and conventional reservoirs. In: SPE Asia Pacific Oil and Gas Conference 2012, SPE 158808 (2012)Google Scholar
  11. 11.
    Klikushin, Y.: Method of fractal classification of compound signals. Radioelectroniks 4, 1–11 (2000)Google Scholar
  12. 12.
    Dake, L.: The Practice of Reservoir Engineering. Elsevier, New York (2001)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • T. Sh. Salavatov
    • 1
  • A. A. Abbasov
    • 2
  • H. Kh. Malikov
    • 1
  • D. F. Guseynova
    • 3
  • A. A. Suleymanov
    • 1
    Email author
  1. 1.Scientific Research Institute “Geotechnological Problems of Oil, Gas and Chemistry”BakuAzerbaijan
  2. 2.Oil and Gas Reservoirs and Reserves Management DepartmentSOCARBakuAzerbaijan
  3. 3.Oil and Gas Research and Design InstituteSOCARBakuAzerbaijan

Personalised recommendations