Estimativa da fração de água em bombas centrífugas submersas utilizando redes neurais artificiais

Author(s)

Matheus Paris Orsi

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica
• Defense date: 2021-12-09

Examining board members

Alberto Luiz Serpa; Flávio Vasconcelos da Silva; Marcelo Souza de Castro

• Advisor: Alberto Luiz Serpa

Abstract

The artificial lift is a method used to obtain a higher oil flow rate from a well, through some scheme that reduces the pressure at the bottomhole. Electrical submersible pumping is a common method in petroleum industry. The main component of this method is the electrical submersible pump (ESP), that can operate with complex flows involving mixtures of oil, water and gas. The presence of water in oil fields is a problem because it favors the formation of emulsions, which are the mixture of oil and water. Emulsions can be found in the form of oil-in-water and water-in-oil emulsions, depending on which phase is the continuous one and which is the dispersed one. Water-in-oil emulsions increase considerably the viscosity of the mixture and affect the pump’s efficiency, diminishing its pumping capacity. The increase or decrease of the water fraction in the process may cause the phenomenon called catastrophic phase inversion (CPI), in which the dispersed phase becomes the continuous one and rapidly alters the physical properties of the flow, causing operational instability throughout the production system. In order to identify and predict this important phenomenon in complex multiphase flows, the usage of advanced identification tools, based on experimental data, has been used in recent years. In this work, artificial neural networks are used to estimate the water fraction in a flow that runs through an ESP. For that, data like inlet and outlet pressures, temperature and the correspondent water cut values, among others, were collected from an ESP operating with water and oil. Single-phase and two-phase tests were performed with the purpose of collecting data with different water cut values, ranging from ?0% (single-phase oil) to ?100% (two-phase water and oil). From the laboratory experiments, it was possible to build a data-driven computational tool capable of estimating the water fraction that runs through the pump, based on an optimized artificial neural network structure, which achieved a coefficient of determination (R-squared) value of 0.99929 and 0.99468 for the training and test datasets, respectively. Also, field data provided by Equinor Brazil were analyzed before and after its cleaning and an ANN model was built to predict the BSW (basic sediments and water) fractions along the system’s functioning. With clean data the model achieved an R-squared value of 0.99883 and 0.99884 on the training and test datasets, respectively (AU)

• FAPESP's process: 19/08446-4 - Water cut identification in multiphase flows using centrifugal pumps
• Grantee: Matheus Paris Orsi
• Support Opportunities: Scholarships in Brazil - Master