ISSN:2164-6457 (print)
ISSN:2164-6473 (online)
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu

Artificial Neural Networks in Oil Production Problems

Journal of Applied Nonlinear Dynamics 3(4) (2014) 299--306 | DOI:10.5890/JAND.2014.12.001

Yuliya Lind$^{1}$, Jan Awrejcewicz$^{2}$ , Aigul Kabirova$^{3}$, Azamat Murzagalin$^{4}$, Anna Khashper$^{4}$

$^{1}$ BashNIPIneft LLC, Ufa, Russia

$^{2}$ Department of Automation, Biomechanics and Mechatronics, Technical University of Lodz, Lodz, Poland

$^{3}$ Laboratory of Mathematical Chemistry, Institute of Petrochemistry and Catalysis of RAS, Ufa, Russia

$^{4}$ Department of Mathematics and Information Technologies, Bashkir State University, Ufa, Russia

Abstract

The general approach to engineering of systems in oil and gas industry from the aspect of their automation and use of information technologies including design and experiment result analysis on the base of mathematical models requires involving newest technologies of artificial intelligence to obtain the most effective results. In this paper application of artificial intelligence methods such as neural networks for optimization of drilling process and automation of log curves digitization has been proposed. A hybrid neural network on the base of radial basis network learning by k-means algorithm has demonstrated the highest efficiency for solution of these problems regarding classification and pattern recognition.

Acknowledgments

The paper has been presented during 12th Conference on Dynamical Systems—Theory and Applications.

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