Regular Article
Multiphase gas-flow model of an electrical submersible pump
1
Department of Computational Mechanics, UNICAMP-University of Campinas, São Paulo, Brazil
2
Students-Agreement Graduate - PEC-PG, CAPES/CNPq, Brazil
* Corresponding author: dmartinez628@gmail.com
Received:
7
December
2016
Accepted:
2
July
2018
Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.
© D. Martinez Ricardo et al., published by IFP Energies nouvelles, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
