Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

A.M. Sagdatullin

doi:10.1051/e3sconf/201912405031

All issues

Volume 124 (2019)

E3S Web Conf., 124 (2019) 05031

References

Open Access

Issue		E3S Web Conf. Volume 124, 2019 International Scientific and Technical Conference Smart Energy Systems 2019 (SES-2019)


Article Number		05031
Number of page(s)		4
Section		Additional papers
DOI		https://doi.org/10.1051/e3sconf/201912405031
Published online		10 February 2020

W.S. McCulloch, W. Pitts, A logical calculus of ideas imminent in nervous activity Bull. Math. Biophys, 5, 115–133 (1943) [Google Scholar]
M. Saerens, A. Soquet, Neural controller based on back-propagation algorithm, IEE Proc. F Radar and Signal Processing, 138, 55–62 (1991) [CrossRef] [Google Scholar]
J. Hopfield, D. Tank, Neural computation of decisions optimization problems, Biological Cybernetics, 52, 141–152 (1985) [PubMed] [Google Scholar]
G. Carpenter, S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network, IEEE Computer, 21, 77–88 (1988) [CrossRef] [Google Scholar]
A. Michel, J. Farrell, Associative memories via artificial neural networks, IEEE Control Systems Magazine, 10, 6–17 (1990) [CrossRef] [Google Scholar]
K.J. Hunt, D. Sbarbaro, R. Żbikowski, P.J. Gawthrop, Neural networks for control systems: a survey Automatica, Journal of IFAC, 28, 1083–112 (1992) [Google Scholar]
K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans Neural Networks, 1, 4–27 (1990) [CrossRef] [Google Scholar]
D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representation by error propagation, Parallel distributed processing: explorations in the microstructure of cognition, 1, 318–362 (1986) [Google Scholar]
A. Kayashev, E. Muravyova, M. Sharipov, A. Emekeev, A. Sagdatullin, Verbally defined processes controlled by fuzzy controllers with input/output parameters represented by set of precise terms, Proceedings of 2014 International Conference on Mechanical Engineering, Automation and Control Systems, MEACS 2014, 6986847 (2014) [Google Scholar]
M. Minsky, S. Papert, Perceptrons, Expanded Edition: An introduction to computational geometry, 308 (1988) [Google Scholar]
P. Vas, Vector control of AC machines (1990) [Google Scholar]
A.U. Levin, K.S. Narendra, Control of nonlinear dynamical systems using neural networks: controllability and stabilization, IEEE Transactions on Neural Networks, 4, 192–206 (1993) [CrossRef] [PubMed] [Google Scholar]
D.H. Nguyen, B. Widrow, Neural networks for selflearning control systems, IEEE Control Systems Magazine, 10, 18–23 (1991) [CrossRef] [Google Scholar]
A.V. Basharin, V.A. Novikov, G.G. Sokolovskiy, Control of electrical drives: Textbook for higher educational institution, 392 (1982) [Google Scholar]
R.A. Marchi, F.J. Von Zuben, E. Bim, A neural network approach for the direct power control of a doubly fed induction generator, XI Brazilian Power Electronics Conference (2011) [Google Scholar]
S. Haykin, Neural Networks and Learning Machines, 906 (2009) [Google Scholar]
A.M. Sagdatullin, Development and Modeling of Automation and Control System of Sucker-Rod Well Pump with Beam Drive (2016) https://doi.org/10.1007/s10556-016-0142-4 [Google Scholar]
Y. Djeriri, A. Meroufel, M. Allam, Artificial neural network-based robust tracking control for doubly fed induction generator used in wind energy conversion systems, Journal of Advanced Research in Science and Technology, 2, 173–181 (2015) [Google Scholar]
P.J. Werbos, Neural networks, system identification, and control in the chemical process industries, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, 10(A), 283–356 (2015) [Google Scholar]
J.A. Leonard, M.A. Kramer, Classifying process behaviour with neural networks:strategies for improved training and generalization, American Control Conference, 3, 2478–83 (1990) [Google Scholar]
L. Ljung, T. Söderström, Theory and practice of recursive identification, 501 (1985) [Google Scholar]
P.A. Lant, M.J. Willis, G.A. Montague, M.T. Tham, A.J. Morris, A comparison of adaptive estimation with neural based techniques for bioprocess application, Proceedings of the American Control Conference, 21, 2173–78 (1990) [Google Scholar]
M. Willis, D.C. Massimo, G. Montague, M. Tham, J. Morris, Artificial neural networks in process engineering, Control Theory and Applications, IEE Proceedings, 138, 256–266 (1991) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] W.S. McCulloch, W. Pitts, A logical calculus of ideas imminent in nervous activity Bull. Math. Biophys, 5, 115–133 (1943) [Google Scholar]

[2] M. Saerens, A. Soquet, Neural controller based on back-propagation algorithm, IEE Proc. F Radar and Signal Processing, 138, 55–62 (1991) [CrossRef] [Google Scholar]

[3] J. Hopfield, D. Tank, Neural computation of decisions optimization problems, Biological Cybernetics, 52, 141–152 (1985) [PubMed] [Google Scholar]

[4] G. Carpenter, S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network, IEEE Computer, 21, 77–88 (1988) [CrossRef] [Google Scholar]

[5] A. Michel, J. Farrell, Associative memories via artificial neural networks, IEEE Control Systems Magazine, 10, 6–17 (1990) [CrossRef] [Google Scholar]

[6] K.J. Hunt, D. Sbarbaro, R. Żbikowski, P.J. Gawthrop, Neural networks for control systems: a survey Automatica, Journal of IFAC, 28, 1083–112 (1992) [Google Scholar]

[7] K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans Neural Networks, 1, 4–27 (1990) [CrossRef] [Google Scholar]

[8] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representation by error propagation, Parallel distributed processing: explorations in the microstructure of cognition, 1, 318–362 (1986) [Google Scholar]

[9] A. Kayashev, E. Muravyova, M. Sharipov, A. Emekeev, A. Sagdatullin, Verbally defined processes controlled by fuzzy controllers with input/output parameters represented by set of precise terms, Proceedings of 2014 International Conference on Mechanical Engineering, Automation and Control Systems, MEACS 2014, 6986847 (2014) [Google Scholar]

[10] M. Minsky, S. Papert, Perceptrons, Expanded Edition: An introduction to computational geometry, 308 (1988) [Google Scholar]

[11] P. Vas, Vector control of AC machines (1990) [Google Scholar]

[12] A.U. Levin, K.S. Narendra, Control of nonlinear dynamical systems using neural networks: controllability and stabilization, IEEE Transactions on Neural Networks, 4, 192–206 (1993) [CrossRef] [PubMed] [Google Scholar]

[13] D.H. Nguyen, B. Widrow, Neural networks for selflearning control systems, IEEE Control Systems Magazine, 10, 18–23 (1991) [CrossRef] [Google Scholar]

[14] A.V. Basharin, V.A. Novikov, G.G. Sokolovskiy, Control of electrical drives: Textbook for higher educational institution, 392 (1982) [Google Scholar]

[15] R.A. Marchi, F.J. Von Zuben, E. Bim, A neural network approach for the direct power control of a doubly fed induction generator, XI Brazilian Power Electronics Conference (2011) [Google Scholar]

[16] S. Haykin, Neural Networks and Learning Machines, 906 (2009) [Google Scholar]

[17] A.M. Sagdatullin, Development and Modeling of Automation and Control System of Sucker-Rod Well Pump with Beam Drive (2016) https://doi.org/10.1007/s10556-016-0142-4 [Google Scholar]

[18] Y. Djeriri, A. Meroufel, M. Allam, Artificial neural network-based robust tracking control for doubly fed induction generator used in wind energy conversion systems, Journal of Advanced Research in Science and Technology, 2, 173–181 (2015) [Google Scholar]

[19] P.J. Werbos, Neural networks, system identification, and control in the chemical process industries, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, 10(A), 283–356 (2015) [Google Scholar]

[20] J.A. Leonard, M.A. Kramer, Classifying process behaviour with neural networks:strategies for improved training and generalization, American Control Conference, 3, 2478–83 (1990) [Google Scholar]

[21] L. Ljung, T. Söderström, Theory and practice of recursive identification, 501 (1985) [Google Scholar]

[22] P.A. Lant, M.J. Willis, G.A. Montague, M.T. Tham, A.J. Morris, A comparison of adaptive estimation with neural based techniques for bioprocess application, Proceedings of the American Control Conference, 21, 2173–78 (1990) [Google Scholar]

[23] M. Willis, D.C. Massimo, G. Montague, M. Tham, J. Morris, Artificial neural networks in process engineering, Control Theory and Applications, IEE Proceedings, 138, 256–266 (1991) [CrossRef] [Google Scholar]