An Echo State Network-Based Soft Sensor of Downhole Pressure for a Gas-Lift Oil Well
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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.
KeywordsEcho state network Soft sensor Gas-lift oil well Reservoir computing
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- 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
- 4.Eck, J., et al.: Downhole monitoring: the story so far. Oilfield Review 11(3), 18–29 (1999)Google Scholar
- 6.Billings, S.A.: Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley (2013)Google Scholar
- 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
- 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.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.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.Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, August 2006Google Scholar
- 15.Tychonoff, A., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Winston & Sons, Washington (1977)Google Scholar