Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo
Ano de defesa: | 2011 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Sergipe
|
Programa de Pós-Graduação: |
Pós-Graduação em Engenharia Química
|
Departamento: |
Não Informado pela instituição
|
País: |
BR
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | https://ri.ufs.br/handle/riufs/5049 |
Resumo: | The multiphase flow is a subject that encloses a vast field of knowledge and applications, different technological contexts, different scales, and is target of relatively recent studies. As basic examples there are industrial transport processes as water-vapor, fluidized beds and transport of oil. It can be said that, amongst these systems, the oil transport is presented as classic example of the multiphase flow, therefore can be observed on it all the complexities: flow that involves all the possible phases, that is, solid-liquid-liquid-gas, for particles in suspension (silicon, resins and asphaltenes, metallic composites and salts), oil (liquid hydrocarbons), water and gas (gaseous hydrocarbons), respectively. However, it must be detached that the multiphase flow usually is dealt with some assumptions. The knowledge of the multiphase flow characteristics also is basic for the equipment development of fluids properties measurement on-line, as well as measurement of outflow and pressure, variable of basic interest for the management of reservoirs, quantitative transference control of fluids produced between producer and purchaser, management control of emptyings, fiscalization, amongst others. This work presents a methodology with the use of artificial intelligence techniques, specifically those basing on Artificial Neural Network - ANN's, to predict pressure drop and gradient pressure in multiphase flow, assuming the Black Oil physical model, for different gaseous phase mass fractions in the start of the flow, taking in account properties of the flow, such as viscosities of the individual phases and the mixture, specific mass and speeds of the phases, emphasizing itself flow situations that occur in the oil industry. For the definition of the ANN's architectures and training algorithms it was used data gotten with the deterministic models solutions. It was used, specifically, the deterministic homogeneous and separated flow models. The simulations gotten with the ANN s used had been compared with those solutions gotten with the deterministic models, verifying itself that the used methodology presents satisfactory precision and simplicity of use, compatible with the necessities of the oil industry, being able the boarding to be extended to the situations where operational data are available. |
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Souza, Anderson Dantas dehttp://lattes.cnpq.br/3987314871651010Santana, Pedro Leite dehttp://lattes.cnpq.br/99187849194786882017-09-26T18:10:23Z2017-09-26T18:10:23Z2011-08-01https://ri.ufs.br/handle/riufs/5049The multiphase flow is a subject that encloses a vast field of knowledge and applications, different technological contexts, different scales, and is target of relatively recent studies. As basic examples there are industrial transport processes as water-vapor, fluidized beds and transport of oil. It can be said that, amongst these systems, the oil transport is presented as classic example of the multiphase flow, therefore can be observed on it all the complexities: flow that involves all the possible phases, that is, solid-liquid-liquid-gas, for particles in suspension (silicon, resins and asphaltenes, metallic composites and salts), oil (liquid hydrocarbons), water and gas (gaseous hydrocarbons), respectively. However, it must be detached that the multiphase flow usually is dealt with some assumptions. The knowledge of the multiphase flow characteristics also is basic for the equipment development of fluids properties measurement on-line, as well as measurement of outflow and pressure, variable of basic interest for the management of reservoirs, quantitative transference control of fluids produced between producer and purchaser, management control of emptyings, fiscalization, amongst others. This work presents a methodology with the use of artificial intelligence techniques, specifically those basing on Artificial Neural Network - ANN's, to predict pressure drop and gradient pressure in multiphase flow, assuming the Black Oil physical model, for different gaseous phase mass fractions in the start of the flow, taking in account properties of the flow, such as viscosities of the individual phases and the mixture, specific mass and speeds of the phases, emphasizing itself flow situations that occur in the oil industry. For the definition of the ANN's architectures and training algorithms it was used data gotten with the deterministic models solutions. It was used, specifically, the deterministic homogeneous and separated flow models. The simulations gotten with the ANN s used had been compared with those solutions gotten with the deterministic models, verifying itself that the used methodology presents satisfactory precision and simplicity of use, compatible with the necessities of the oil industry, being able the boarding to be extended to the situations where operational data are available.O escoamento multifásico é um tema que abrange um vasto campo de conhecimentos e aplicações, contextos tecnológicos diferentes, diferentes escalas e é alvo de estudos relativamente recentes. Como exemplos básicos, têm-se os processos de transporte industriais como água-vapor, leitos fluidizados e escoamento de petróleo. Pode-se dizer que, dentre esses sistemas, o transporte de petróleo apresenta-se como exemplo clássico do escoamento multifásico, encontrando-se nele todas as complexidades: escoamento que envolve todas as fases possíveis, ou seja, sólido-líquido-líquido-gás, por partículas em suspensão (sílica, resinas e asfaltenos, compostos metálicos e sais), óleo (hidrocarbonetos líquidos), água e gás (hidrocarbonetos gasosos), respectivamente. Entretanto, deve-se destacar que o escoamento multifásico é costumeiramente tratado com algumas simplificações. O conhecimento das características do escoamento multifásico também é fundamental para o desenvolvimento de equipamentos de medição de propriedades dos fluidos em linha, bem como medição de vazão e pressão, variáveis de fundamental interesse para o gerenciamento de reservatórios, controle de transferência quantitativa dos fluidos produzidos entre produtor e comprador, gerenciamento de controle de vazamentos, fiscalização, dentre outros. Este trabalho apresenta uma metodologia com o uso de técnicas de inteligência artificial, especificamente aquelas baseadas em Redes Neurais Artificiais RNA s, para predizer a perda de carga e o gradiente de pressão em escoamento multifásico, considerando-se o modelo físico Black Oil, para diferentes frações mássicas de fase gasosa no início do escoamento, levando-se em conta propriedades do fluxo, tais como viscosidades das fases individuais e da mistura, massa específica e velocidades das fases, enfatizando-se situações de escoamento que ocorrem na indústria do petróleo. Para a definição das arquiteturas e treinamento das RNA s, foram usados dados obtidos com a solução de modelos determinísticos. Foram usados, especificamente, os modelos determinísticos de escoamento homogêneo e de escoamento separado. Os resultados obtidos com as RNA s foram comparados com aqueles obtidos com os modelos determinísticos, verificando-se que a metodologia usada apresenta precisão satisfatória e simplicidade de uso, compatíveis com as necessidades da indústria petrolífera, podendo a abordagem ser estendida a situações onde dados operacionais são disponíveis.Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorapplication/pdfporUniversidade Federal de SergipePós-Graduação em Engenharia QuímicaUFSBREscoamento multifásicoEscoamento de petróleoModelagem matemáticaRedes neuraisMultiphase flowPetroleum flowMathematical modelingNeural networksCNPQ::ENGENHARIAS::ENGENHARIA QUIMICAEstudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleoStudy loss in multiphase flow using artificial intelligence techniques with emphasis on the flow of oilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSinstname:Universidade Federal de Sergipe (UFS)instacron:UFSORIGINALANDERSON_DANTAS_SOUZA.pdfapplication/pdf1642169https://ri.ufs.br/jspui/bitstream/riufs/5049/1/ANDERSON_DANTAS_SOUZA.pdfbf587a29ae88be6772a712d38dd7300eMD51TEXTANDERSON_DANTAS_SOUZA.pdf.txtANDERSON_DANTAS_SOUZA.pdf.txtExtracted texttext/plain234611https://ri.ufs.br/jspui/bitstream/riufs/5049/2/ANDERSON_DANTAS_SOUZA.pdf.txt780307226b1a0a69f64a0e9f012b8629MD52THUMBNAILANDERSON_DANTAS_SOUZA.pdf.jpgANDERSON_DANTAS_SOUZA.pdf.jpgGenerated Thumbnailimage/jpeg1216https://ri.ufs.br/jspui/bitstream/riufs/5049/3/ANDERSON_DANTAS_SOUZA.pdf.jpg76500630a6f03d77a5baf771648d2812MD53riufs/50492017-11-27 21:47:39.057oai:ufs.br:riufs/5049Repositório InstitucionalPUBhttps://ri.ufs.br/oai/requestrepositorio@academico.ufs.bropendoar:2017-11-28T00:47:39Repositório Institucional da UFS - Universidade Federal de Sergipe (UFS)false |
dc.title.por.fl_str_mv |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
dc.title.alternative.eng.fl_str_mv |
Study loss in multiphase flow using artificial intelligence techniques with emphasis on the flow of oil |
title |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
spellingShingle |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo Souza, Anderson Dantas de Escoamento multifásico Escoamento de petróleo Modelagem matemática Redes neurais Multiphase flow Petroleum flow Mathematical modeling Neural networks CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA |
title_short |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
title_full |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
title_fullStr |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
title_full_unstemmed |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
title_sort |
Estudo da perda de carga em escoamento multifásico utilizando técnicas de inteligência artificial com ênfase no escoamento de petróleo |
author |
Souza, Anderson Dantas de |
author_facet |
Souza, Anderson Dantas de |
author_role |
author |
dc.contributor.author.fl_str_mv |
Souza, Anderson Dantas de |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3987314871651010 |
dc.contributor.advisor1.fl_str_mv |
Santana, Pedro Leite de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9918784919478688 |
contributor_str_mv |
Santana, Pedro Leite de |
dc.subject.por.fl_str_mv |
Escoamento multifásico Escoamento de petróleo Modelagem matemática Redes neurais |
topic |
Escoamento multifásico Escoamento de petróleo Modelagem matemática Redes neurais Multiphase flow Petroleum flow Mathematical modeling Neural networks CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA |
dc.subject.eng.fl_str_mv |
Multiphase flow Petroleum flow Mathematical modeling Neural networks |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA |
description |
The multiphase flow is a subject that encloses a vast field of knowledge and applications, different technological contexts, different scales, and is target of relatively recent studies. As basic examples there are industrial transport processes as water-vapor, fluidized beds and transport of oil. It can be said that, amongst these systems, the oil transport is presented as classic example of the multiphase flow, therefore can be observed on it all the complexities: flow that involves all the possible phases, that is, solid-liquid-liquid-gas, for particles in suspension (silicon, resins and asphaltenes, metallic composites and salts), oil (liquid hydrocarbons), water and gas (gaseous hydrocarbons), respectively. However, it must be detached that the multiphase flow usually is dealt with some assumptions. The knowledge of the multiphase flow characteristics also is basic for the equipment development of fluids properties measurement on-line, as well as measurement of outflow and pressure, variable of basic interest for the management of reservoirs, quantitative transference control of fluids produced between producer and purchaser, management control of emptyings, fiscalization, amongst others. This work presents a methodology with the use of artificial intelligence techniques, specifically those basing on Artificial Neural Network - ANN's, to predict pressure drop and gradient pressure in multiphase flow, assuming the Black Oil physical model, for different gaseous phase mass fractions in the start of the flow, taking in account properties of the flow, such as viscosities of the individual phases and the mixture, specific mass and speeds of the phases, emphasizing itself flow situations that occur in the oil industry. For the definition of the ANN's architectures and training algorithms it was used data gotten with the deterministic models solutions. It was used, specifically, the deterministic homogeneous and separated flow models. The simulations gotten with the ANN s used had been compared with those solutions gotten with the deterministic models, verifying itself that the used methodology presents satisfactory precision and simplicity of use, compatible with the necessities of the oil industry, being able the boarding to be extended to the situations where operational data are available. |
publishDate |
2011 |
dc.date.issued.fl_str_mv |
2011-08-01 |
dc.date.accessioned.fl_str_mv |
2017-09-26T18:10:23Z |
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2017-09-26T18:10:23Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
format |
masterThesis |
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publishedVersion |
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https://ri.ufs.br/handle/riufs/5049 |
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https://ri.ufs.br/handle/riufs/5049 |
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por |
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openAccess |
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Universidade Federal de Sergipe |
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Pós-Graduação em Engenharia Química |
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UFS |
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BR |
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Universidade Federal de Sergipe |
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