Eventos Anais de eventos
COBEM 2021
26th International Congress of Mechanical Engineering
Water Cut Estimation in Electrical Submersible Pumps Using Neural Networks
Submission Author:
Matheus Orsi , SP
Co-Authors:
Matheus Orsi, Alberto Luiz Serpa, Jorge Luiz Biazussi
Presenter: Matheus Orsi
doi://10.26678/ABCM.COBEM2021.COB2021-1618
Abstract
An artificial lift is a method used to obtain a higher oil flow rate from the 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, vibration 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 an R-score of 0.9987.
Keywords
machine learning, Electric Submersible Pump, Liquid-liquid flow, Artificial neural networks, System Identification
