Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells

Author:

Antonelo Eric A.,Camponogara Eduardo,Foss Bjarne

Funder

CNPq

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference43 articles.

1. Observer design for multiphase flow in vertical pipes with gas-lift—theory and experiments;Aamo;Journal of Process Control,2005

2. Antonelo, E.A., & Camponogara, E. (2015). An echo state network-based soft sensor of downhole pressure for a gas-lift oil well. In 16th international conference on engineering applications of neural networks.

3. Antonelo, E.A., Camponogara, E., & Plucenio, A. (2015). System identification of a vertical riser model with echo state networks. In 2nd IFAC workshop on automatic control in offshore oil and gas production.

4. Learning slow features with reservoir computing for biologically-inspired robot localization;Antonelo;Neural Networks,2012

5. On learning navigation behaviors for small mobile robots with reservoir computing architectures;Antonelo;IEEE Transactions on Neural Networks and Learning Systems,2015

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