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An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well

on Thu, 09/29/2016 - 19:51
TitleAn Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well
Publication TypeConference Proceedings
Year of Conference2015
AuthorsAntonelo EA, Camponogara E
Conference Name16th International Conference on Engineering Applications of Neural Networks
VolumeCommunications in Computer and Information Science, vol 517
ISBN Number978-3-319-23981-1

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.