Abstract
For a long time there has been a tendency to increase field productivity, therefore, increasing oil recovery is the main task for fuel and energy complex. Currently, neural networks are increasingly used in various industries. The advantage of neural networks is to work with a large amount of data, however, it must have sufficient data sets collected and prepared for its operation, thereby achieving high decision accuracy. When developing oil and gas fields, the main task is to ensure maximum production from an economic and physical point of view. Oil production at oil and gas fields varies in volume, complexity, operating conditions, etc., therefore, it is necessary to find the optimal production conditions for each field. At the moment, the main problems in oil production at oil and gas fields are: the long processing time of data collected from wells, the increased risks of operating these wells, as well as the low amount of oil produced. The main objective of this study is to develop a control method us in artificial intelligence to control the production process in oil and gas fields, taking in to account all factors, in order to maximize oil production. In the course of this study, direct transmission to the neural network was obtained, which allows oil to be extracted at oil and gas fields, taking into account all factors. The resulting neural network, without reconfiguring weighted connections, generates output signals when applied to the input to the network.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Muravyova, E.: Autoregressive neural network for model predictive control of multivariable cracking catalyst calcinatory. Opt. Mem. Neural Netw. 213–216 (2011)
Nielsen, M.: Neural Networks and Deep Learning. Determination Press (2016)
Rashid, T.: Make Your Own Neural Network. CreateSpace (2016)
Muravyova, E.A., Timerbaev, R.R.: Application of artificial neural networks in the process of catalytic cracking. Opt. Mem. Neural Netw. 27(3), 203–208 (2018)
Muravyova, E.A., Uspenskaya, N.N.: Development of a neural network for a boiler unit generating water vapour control. Opt. Mem. Neural Netw. 27(4), 297–307 (2018)
Kriesel, D.: A Brief Introduction to Neural Networks. Autoedicin, Bonn (2014)
Samarasinghe, S.: Neural Networks for Applied Sciences and Engineering. CRC Press, Boston (2016)
Evmenov, V.: Intelligent Control Systems. Librokom, Moscow (2009)
Muravyova, E., Almakaev, I.: Electrical heating reactor control system using neural network and the fuzzy controller. In: International Multi-Conference on Industrial Engineering and Modern, Vladivostok, 3–4 October 2018, pp. 1–6 (2019). https://doi.org/10.1109/FarEastCon.2019.8934383
Muravyova, E., Sharinov, M.: Intelligent control system for process parameters based on a neural network. In: 14th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, pp. 256–260 (2018)
Haikin, S.: Neural Networks: A Complete Course. Williams, Moscow (2006)
Callan, R.: Basic Concepts of Neural Networks. Williams, Moscow (2003)
Nikolenko, S., Kadurin, A., Arkhangelsk, E.: Deep learning. Immersion in the world of neural networks. Peter, Saint-Petersburg (2018)
Muraveva, E., Kayashev, A., Gabitov, R.: Control of the furnace for calcining zeolite-containing catalysts for cracking petroleum products using the floating horizon method using a neural network model. Automation, telemechanization and communication in the oil industry, p. 19 (2010)
Mahmud, F., Tarek, A.: Identification and adaptive control of dynamic non-linear installations. Intell. Control Autom. 02(03), 176–181 (2011)
Li, H., Chen, M.: Design of decoupling PID controller for a kind of practical engineering. Control Eng. 15(3), 275–278 (2008)
Cheng, Q., Zheng, Y.: Multi-variable PID neural network control systems and their applicationto coordination control. East China Electric Power 11, 54–58 (2007)
Vasilyev, V., Ilyasov, B.: Intelligent control systems using genetic algorithms. Appendix J. Inf. Technol. 12, 392 (2000)
Andreychikov, A., Andreychikova, O.: Intelligent Information Systems. Finance and Statistics, Moscow (2004)
Vasilyev, V., Ilyasov, B.: Intelligent Control Systems. Theory and Practice. Radio Engineering, Moscow (2009)
Muravyova, E., Gabitov, R.: Economic features to optimize the catalyst calcinations process. In: International Multi-Conference on industrial engineering and modern technologies, FarEastCon 2018, Vladivostok, 3–4 October 2018, pp. 1–5 (2019). https://doi.org/10.1109/FarEastCon.2018.8602535
Muravyova, E., Sharipov, M., Gabitov, R.: Scada-system based on multidimensional precise logic controller for the control of a cement kiln. In: 2018 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2018, Vladivostok, 3–4 October 2018, pp. 1–6 (2019). https://doi.org/10.1109/FarEastCon.2018.8602589
Muravyova, E., Sharipov, M., Bondarev, A.: Method for increasing the speed and reducing the error of multidimensional precise logic controller. In: International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2018, Vladivostok, 3–4 October 2018, pp. 1–8 (2019). https://doi.org/10.1109/FarEastCon.2018.8602643
Heoand, S., Lee, H.: Parallel neural networks for improved nonlinear principal component analysis. Comput. Chem. Eng. 1274, 1–0 (2019)
Huang, S., Zhang, J., Hu, C.: Effects of external stimulations on transition behaviors in neural network with time-delay. Physica A: Stat. Mech. Appl. 53615 (2019). https://doi.org/10.1016/j.physa.2019.122517
Sheng, D., Wei, Y., Chen, Y., et al.: Convolutional neural networks with fractional order gradient method. Neurocomputing (2019). https://doi.org/10.1016/j.neucom.2019.10.017
Lin, S., Huang, Y., Ren, S.: Analysis and pinning control for passivity of coupled different dimensional neural networks. Neurocomputing 32110, 187–200 (2018)
Kobayashi, M.: Twin-multistate commutative quaternion Hopfield neural networks. Neurocomputing 3203, 150–156 (2018)
Berg, J., Nystrom, K.: A unified deep artificial neural network approach to partial differential equations in complex geometries. Neurocomputing 31723, 28–41 (2018)
Jiang, N., Xu, J., Zhang, S.: Neural network control of networked redundant manipulator system with weight initialization method. Neurocomputing 30713, 117–129 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sharipov, M.I. (2021). Development of a Method for Controlling the Production Process in Oil and Gas Fields Using Neural Networks. In: Radionov, A.A., Gasiyarov, V.R. (eds) Advances in Automation II. RusAutoCon 2020. Lecture Notes in Electrical Engineering, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-030-71119-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-71119-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71118-4
Online ISBN: 978-3-030-71119-1
eBook Packages: EngineeringEngineering (R0)