Reference : An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36467
An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well
English
Antonelo, Eric Aislan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Camponogara, Eduardo [> >]
2015
Engineering Applications of Neural Networks
Iliadis, Lazaros
Jayne, Chrisina
Springer
Communications in Computer and Information Science, vol 517.
379-389
Yes
International
978-3-319-23981-1
16th International Conference on Engineering Applications of Neural Networks
25-09-2015 to 28-09-2015
[en] Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ei- ther has a high probability of failure or is unreliable due to harsh environ- ment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and tempera- ture 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 power- ful 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 read- out output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting.
http://hdl.handle.net/10993/36467
10.1007/978-3-319-23983-5_35
https://link.springer.com/chapter/10.1007/978-3-319-23983-5_35

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