Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Haken, H.: Synergetics: Introduction and Advanced Topics. Springer, Berlin (2004)
Feder, E.: Fractals. Plenum Press, New York (1988)
Mirzajanzadeh, A., Aliev, N., Yusifzade, K.: Fragments on Development of Offshore Oil and Gas Fields. Elm, Baku (1997)
Mirzajanzadeh, A., Hasanov, M., Bahtizin, R.: Modeling of Oil and Gas Production Processes. ICR, Moscow (2004)
Bendat, J., Piersol, A.: Random Data: Analysis and Measurements Procedures. Wiley, New York (1971)
Mirzajanzadeh, A., Sultanov, Ch.: Reservoir Oil Recovery Process Diacoptics. APC, Baku (1995)
Jensen, J., Lake, L., Corbett, P., Goggin, D.: Statistics for Petroleum Engineers and Geoscientists. Elsevier, Amsterdam (2000)
Mandelbrot, B.: Fractals, hasard et finance, 246 p. Flammarion, Paris (1997)
Belfield, W.C.: Incorporating spatial distribution into stochastic modeling of fractures: multifractals and levy-stable statistics. J. Struct. Geol. 20(4), 473–486 (1998)
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)
Klikushin, Y.: Method of fractal classification of compound signals. Radioelectroniks 4, 1–11 (2000)
Dake, L.: The Practice of Reservoir Engineering. Elsevier, New York (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Salavatov, T.S., Abbasov, A.A., Malikov, H.K., Guseynova, D.F., Suleymanov, A.A. (2019). Non-parametric Criteria of Chaotic Data Analysis in Oil Production. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_66
Download citation
DOI: https://doi.org/10.1007/978-3-030-04164-9_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04163-2
Online ISBN: 978-3-030-04164-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)