The measurement of the water component in multiphase mixtures is key in the Oil and Gas industry. My thesis work was developed in Pietro Fiorentini Spa which is a company who provides services to the oil and gas industries from the extraction to the transport at the final user. These services can includes gas meters for the houses, remote control of a gas pipeline and extraction analysis. Actually Pietro Fiorentini Spa is developing a microwave sensor that measures the complex permittivity of the mixture in order to compute the water fraction in the extraction process of an oil wells. The final measurement accuracy also depends on the modelling of the multiphase mixture (liquid and gas). After the study of the application, the Thesis foresees the improvement of the analytical model in use through the analysis of available data. Finally, a novel data-driven analysis approach with "machine learning" techniques will be developed and compared to the previous

Water detection in multiphase mixtures

Simionato, Marco
2020/2021

Abstract

The measurement of the water component in multiphase mixtures is key in the Oil and Gas industry. My thesis work was developed in Pietro Fiorentini Spa which is a company who provides services to the oil and gas industries from the extraction to the transport at the final user. These services can includes gas meters for the houses, remote control of a gas pipeline and extraction analysis. Actually Pietro Fiorentini Spa is developing a microwave sensor that measures the complex permittivity of the mixture in order to compute the water fraction in the extraction process of an oil wells. The final measurement accuracy also depends on the modelling of the multiphase mixture (liquid and gas). After the study of the application, the Thesis foresees the improvement of the analytical model in use through the analysis of available data. Finally, a novel data-driven analysis approach with "machine learning" techniques will be developed and compared to the previous
2020-11
89
Multiphase mixtures, permittivity,wet gas, machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/28844