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Water cut identification in multiphase flows using centrifugal pumps

Grant number: 19/08446-4
Support type:Scholarships in Brazil - Master
Effective date (Start): August 01, 2019
Effective date (End): September 30, 2021
Field of knowledge:Engineering - Mechanical Engineering - Transport Phenomena
Cooperation agreement: Equinor (former Statoil)
Principal researcher:Alberto Luiz Serpa
Grantee:Matheus Paris Orsi
Home Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:17/15736-3 - Engineering Research Centre in Reservoir and Production Management, AP.PCPE

Abstract

The suitable operation of electrical submersible centrifugal pumps in complex flows such as those involving different liquids (water and oil) is important in the process of artificial elevation to ensure adequate process efficiency and to identify changes in the system behavior to plan maintenance and suitable operating conditions to obtain good levels of reliability. This master's project involves the use of systems identification techniques to predict, through pump operating parameters such as rotation, electric current, torque, pressure variation and others, the respective flow pattern identifying the water cut or, in other words, the percentage of water present on the flow. The centrifugal pumps used in this type of artificial elevation process have their characteristic curves determined by the manufacturer using water as working fluid. In the case of flows that are more complex, involving water and oil, the operating conditions of the pump change significantly and certain operating situations considered inadequate should be avoided. The main difficulty of pumping these two immiscible liquids is the formation of emulsions, which increase the viscosity of the mixture,causing loss of pumping efficiency and impair the life of the equipment, as well as increasing the power needed to supply the pump. To identify and predict this condition in multiphase flows, the use of systems identification tools based on experimental data becomes relevant. In this master's project, we intend to explore these techniques using classical techniques based on least squares data fitting, techniques based on neural networks and techniques based on the most recently used machine-learning concepts (deep learning) to predict the conditions of operation of the centrifugal pump and thus adjust its operating conditions to avoid undesired operating situations. The main expected result of this master's project is to explore some modeling tools applied to the case of the multiphase flow problem in order to identify the water cut. The experimental results will be obtained from test rigs, available at LABPETRO of UNICAMP, looking for an adequate validation of the techniques employed. (AU)

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