Advertisement

An Echo State Network-Based Soft Sensor of Downhole Pressure for a Gas-Lift Oil Well

  • Eric Aislan AntoneloEmail author
  • Eduardo Camponogara
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

Soft sensor technology has been increasingly used in industry. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor either has a high probability of failure or is unreliable due to harsh environment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and temperature in deepwater oil wells. In this paper, historical data obtained from an actual offshore oil well is used to build a black box model that estimates the PDG downhole pressure from platform variables, using Echo State Networks (ESNs), which are a class of recurrent networks with powerful modeling capabilities. These networks, differently from other neural networks models used by most soft sensors in literature, can model the nonlinear dynamical properties present in the noisy real-world data by using a two-layer structure with efficient training: a recurrent nonlinear layer with fixed randomly generated weights and a linear adaptive readout output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting.

Keywords

Echo state network Soft sensor Gas-lift oil well Reservoir computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G.: Soft sensors for monitoring and control of industrial processes. Springer Science & Business Media (2007)Google Scholar
  2. 2.
    Sbarbaro, D., Ascencio, P., Espinoza, P., Mujica, F., Cortes, G.: Adaptive soft-sensors for on-line particle size estimation in wet grinding circuits. Control Engineering Practice 16(2), 171–178 (2008)CrossRefGoogle Scholar
  3. 3.
    Fujiwara, K., Kano, M., Hasebe, S.: Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design. Control Engineering Practice 20(4), 371–378 (2012)CrossRefGoogle Scholar
  4. 4.
    Eck, J., et al.: Downhole monitoring: the story so far. Oilfield Review 11(3), 18–29 (1999)Google Scholar
  5. 5.
    Teixeira, B.O., Castro, W.S., Teixeira, A.F., Aguirre, L.A.: Data-driven soft sensor of downhole pressure for a gas-lift oil well. Control Eng. Practice 22, 34–43 (2014)CrossRefGoogle Scholar
  6. 6.
    Billings, S.A.: Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley (2013)Google Scholar
  7. 7.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  8. 8.
    Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Antonelo, E.A., Camponogara, E., Plucenio, A.: System identification of a vertical riser model with echo state networks. In: 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production (2015)Google Scholar
  10. 10.
    Jaeger, H., Lukosevicius, M., Popovici, D.: Optimization and applications of echo state networks with leaky integrator neurons. Neur. Netw. 20(3), 335–352 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Schrauwen, B., Defour, J., Verstraeten, D., Van Campenhout, J.: The introduction of time-scales in reservoir computing, applied to isolated digits recognition. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 471–479. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  12. 12.
    Jaeger, H.: The echo state approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology (2001)Google Scholar
  13. 13.
    Verstraeten, D., Dambre, J., Dutoit, X., Schrauwen, B.: Memory versus non-linearity in reservoirs. In: Proc. of the IEEE IJCNN, pp. 1–8, July 2010Google Scholar
  14. 14.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, August 2006Google Scholar
  15. 15.
    Tychonoff, A., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Winston & Sons, Washington (1977)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Automation and Systems EngineeringFederal University of Santa CatarinaFlorianópolisBrazil

Personalised recommendations