Skip to main content

Development of a Method for Controlling the Production Process in Oil and Gas Fields Using Neural Networks

  • Conference paper
  • First Online:
Advances in Automation II (RusAutoCon 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 729))

Included in the following conference series:

  • 492 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Muravyova, E.: Autoregressive neural network for model predictive control of multivariable cracking catalyst calcinatory. Opt. Mem. Neural Netw. 213–216 (2011)

    Google Scholar 

  2. Nielsen, M.: Neural Networks and Deep Learning. Determination Press (2016)

    Google Scholar 

  3. Rashid, T.: Make Your Own Neural Network. CreateSpace (2016)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Kriesel, D.: A Brief Introduction to Neural Networks. Autoedicin, Bonn (2014)

    Google Scholar 

  7. Samarasinghe, S.: Neural Networks for Applied Sciences and Engineering. CRC Press, Boston (2016)

    Book  Google Scholar 

  8. Evmenov, V.: Intelligent Control Systems. Librokom, Moscow (2009)

    Google Scholar 

  9. 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

  10. 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)

    Google Scholar 

  11. Haikin, S.: Neural Networks: A Complete Course. Williams, Moscow (2006)

    Google Scholar 

  12. Callan, R.: Basic Concepts of Neural Networks. Williams, Moscow (2003)

    Google Scholar 

  13. Nikolenko, S., Kadurin, A., Arkhangelsk, E.: Deep learning. Immersion in the world of neural networks. Peter, Saint-Petersburg (2018)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. Mahmud, F., Tarek, A.: Identification and adaptive control of dynamic non-linear installations. Intell. Control Autom. 02(03), 176–181 (2011)

    Article  Google Scholar 

  16. Li, H., Chen, M.: Design of decoupling PID controller for a kind of practical engineering. Control Eng. 15(3), 275–278 (2008)

    Google Scholar 

  17. Cheng, Q., Zheng, Y.: Multi-variable PID neural network control systems and their applicationto coordination control. East China Electric Power 11, 54–58 (2007)

    Google Scholar 

  18. Vasilyev, V., Ilyasov, B.: Intelligent control systems using genetic algorithms. Appendix J. Inf. Technol. 12, 392 (2000)

    Google Scholar 

  19. Andreychikov, A., Andreychikova, O.: Intelligent Information Systems. Finance and Statistics, Moscow (2004)

    Google Scholar 

  20. Vasilyev, V., Ilyasov, B.: Intelligent Control Systems. Theory and Practice. Radio Engineering, Moscow (2009)

    Google Scholar 

  21. 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

  22. 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

  23. 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

  24. Heoand, S., Lee, H.: Parallel neural networks for improved nonlinear principal component analysis. Comput. Chem. Eng. 1274, 1–0 (2019)

    Google Scholar 

  25. 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

  26. 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

    Article  Google Scholar 

  27. Lin, S., Huang, Y., Ren, S.: Analysis and pinning control for passivity of coupled different dimensional neural networks. Neurocomputing 32110, 187–200 (2018)

    Article  Google Scholar 

  28. Kobayashi, M.: Twin-multistate commutative quaternion Hopfield neural networks. Neurocomputing 3203, 150–156 (2018)

    Article  Google Scholar 

  29. Berg, J., Nystrom, K.: A unified deep artificial neural network approach to partial differential equations in complex geometries. Neurocomputing 31723, 28–41 (2018)

    Article  Google Scholar 

  30. Jiang, N., Xu, J., Zhang, S.: Neural network control of networked redundant manipulator system with weight initialization method. Neurocomputing 30713, 117–129 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. I. Sharipov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics