FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
This doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The resea...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Recursos: | Universidade Federal do Espírito Santo (UFES) |
| Repositorio: | Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufes.br:10/17934 |
| Acesso em linha: | http://repositorio.ufes.br/handle/10/17934 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sensores de fibra óptica Redes de bragg em fibras (FBG) Monitoramento de integridade estrutural (SHM) Engenharia Elétrica |
| Resumo: | This doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The research introduces FBG-based for tank structural health monitoring and parameters measurements in fluids, such as temperature, level, thermal conductivity and salinity. Experimental results demonstrate the efficacy of these sensors in challenging industrial conditions. The thermal experiments, utilizing an FBG-based temperature sensor, reveal insights into thermal power distribution in liquid processing systems. Specific heat and thermal conductivity of water are successfully estimated, demonstrating increased thermal stability with higher heat power. A method for measuring heat transfer rate in liquids is proposed, showing potential applications in industrial contexts. In the realm of structural health monitoring (SHM), the quasi-distributed FBG sensor, combined with supervised machine learning, exhibits high accuracy in monitoring stress and deformation in oil tank structures. The Random Forest algorithm enables precise liquid level estimation with minimal error, contributing to predictive maintenance strategies. The development of an all-optical hot-wire sensor showcases its precision in assessing thermal conductivity and salinity in various fluids. The sensor, integrating FBG with a hot-wire component, proves effective in discriminating substances with close thermal conductivity values. Future work aims at reducing measurement times and adapting the sensor for direct salinity measurement. Finally, worth highlighting the significant contributions of each sensor, emphasizing the practical applicability and promising results obtained in thermal analysis, structural health monitoring and all-optical sensing for the oil and gas industry. The research sets the stage for further exploration and refinement of these sensor technologies in complex industrial scenarios. |
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