Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field

Detalhes bibliográficos
Autor(a) principal: Allahyarzadeh-Bidgoli, Ali
Data de Publicação: 2018
Outros Autores: Dezan, Daniel Jonas, Salviano, Leandro Oliveira, de Oliveira, Silvio, Yanagihara, Jurandir Itizo
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/221281
Resumo: A Floating, Production Storage and Offloading (FPSO) plant is a high energy consumer (from a few to several hundreds of megawatts). Thus, a fuel consumption optimization procedure can be applied to find optimum operating conditions of the unit, saving money and CO2 emissions from oil and gas processing companies. In this work, two different operating conditions of the Brazilian deep water oil field in pre-salt areas are investigated for FPSO fuel consumption minimization: (1) 50% BS&W oil content and; (2) high water and CO2 in oil content. The impact of eight thermodynamic input parameters on fuel consumption of the FPSO unit is investigated by the Smoothing Spline ANOVA (SS-ANOVA) method. From SS-ANOVA, the input parameters that presented the highest impact on fuel consumption were selected for analysis in an optimization procedure. The numerical simulations of the whole FPSO unit are performed by using Aspen HYSYS®. The optimization procedure uses a modified Genetic Algorithm, which is a combination of Genetic Algorithm and SQP method. The results from the optimized case indicated that the minimization of fuel consumption is achieved by increasing the operating pressure in the third stage of the separation train and by decreasing the operating temperature in the second stage of the separation train for both operation modes. There was a reduction in power demand of 10.08 % for mode 1 and 2.92 % for mode 2, in comparison to the baseline case. Consequently, the fuel consumption of the plant was decreased by 8.34% for mode 1 and 2.43% for mode 2, when compared to the baseline case.
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spelling Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil fieldDeep-water oil fieldFPSOFuel consumption optimizationGenetic algorithmSQP methodThermodynamic analysisA Floating, Production Storage and Offloading (FPSO) plant is a high energy consumer (from a few to several hundreds of megawatts). Thus, a fuel consumption optimization procedure can be applied to find optimum operating conditions of the unit, saving money and CO2 emissions from oil and gas processing companies. In this work, two different operating conditions of the Brazilian deep water oil field in pre-salt areas are investigated for FPSO fuel consumption minimization: (1) 50% BS&W oil content and; (2) high water and CO2 in oil content. The impact of eight thermodynamic input parameters on fuel consumption of the FPSO unit is investigated by the Smoothing Spline ANOVA (SS-ANOVA) method. From SS-ANOVA, the input parameters that presented the highest impact on fuel consumption were selected for analysis in an optimization procedure. The numerical simulations of the whole FPSO unit are performed by using Aspen HYSYS®. The optimization procedure uses a modified Genetic Algorithm, which is a combination of Genetic Algorithm and SQP method. The results from the optimized case indicated that the minimization of fuel consumption is achieved by increasing the operating pressure in the third stage of the separation train and by decreasing the operating temperature in the second stage of the separation train for both operation modes. There was a reduction in power demand of 10.08 % for mode 1 and 2.92 % for mode 2, in comparison to the baseline case. Consequently, the fuel consumption of the plant was decreased by 8.34% for mode 1 and 2.43% for mode 2, when compared to the baseline case.Dept. of Mechanical Eng. Polytechnic School University of Sao PauloDept. of Energy Eng. Federal University of ABCDept. of Mechanical Eng. State University of São PauloUniversidade de São Paulo (USP)Federal University of ABCAllahyarzadeh-Bidgoli, AliDezan, Daniel JonasSalviano, Leandro Oliveirade Oliveira, SilvioYanagihara, Jurandir Itizo2022-04-28T19:27:07Z2022-04-28T19:27:07Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.http://hdl.handle.net/11449/2212812-s2.0-85064159132Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systemsinfo:eu-repo/semantics/openAccess2022-04-28T19:27:07Zoai:repositorio.unesp.br:11449/221281Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:31:31.048946Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
title Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
spellingShingle Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
Allahyarzadeh-Bidgoli, Ali
Deep-water oil field
FPSO
Fuel consumption optimization
Genetic algorithm
SQP method
Thermodynamic analysis
title_short Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
title_full Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
title_fullStr Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
title_full_unstemmed Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
title_sort Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
author Allahyarzadeh-Bidgoli, Ali
author_facet Allahyarzadeh-Bidgoli, Ali
Dezan, Daniel Jonas
Salviano, Leandro Oliveira
de Oliveira, Silvio
Yanagihara, Jurandir Itizo
author_role author
author2 Dezan, Daniel Jonas
Salviano, Leandro Oliveira
de Oliveira, Silvio
Yanagihara, Jurandir Itizo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Federal University of ABC
dc.contributor.author.fl_str_mv Allahyarzadeh-Bidgoli, Ali
Dezan, Daniel Jonas
Salviano, Leandro Oliveira
de Oliveira, Silvio
Yanagihara, Jurandir Itizo
dc.subject.por.fl_str_mv Deep-water oil field
FPSO
Fuel consumption optimization
Genetic algorithm
SQP method
Thermodynamic analysis
topic Deep-water oil field
FPSO
Fuel consumption optimization
Genetic algorithm
SQP method
Thermodynamic analysis
description A Floating, Production Storage and Offloading (FPSO) plant is a high energy consumer (from a few to several hundreds of megawatts). Thus, a fuel consumption optimization procedure can be applied to find optimum operating conditions of the unit, saving money and CO2 emissions from oil and gas processing companies. In this work, two different operating conditions of the Brazilian deep water oil field in pre-salt areas are investigated for FPSO fuel consumption minimization: (1) 50% BS&W oil content and; (2) high water and CO2 in oil content. The impact of eight thermodynamic input parameters on fuel consumption of the FPSO unit is investigated by the Smoothing Spline ANOVA (SS-ANOVA) method. From SS-ANOVA, the input parameters that presented the highest impact on fuel consumption were selected for analysis in an optimization procedure. The numerical simulations of the whole FPSO unit are performed by using Aspen HYSYS®. The optimization procedure uses a modified Genetic Algorithm, which is a combination of Genetic Algorithm and SQP method. The results from the optimized case indicated that the minimization of fuel consumption is achieved by increasing the operating pressure in the third stage of the separation train and by decreasing the operating temperature in the second stage of the separation train for both operation modes. There was a reduction in power demand of 10.08 % for mode 1 and 2.92 % for mode 2, in comparison to the baseline case. Consequently, the fuel consumption of the plant was decreased by 8.34% for mode 1 and 2.43% for mode 2, when compared to the baseline case.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2022-04-28T19:27:07Z
2022-04-28T19:27:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv ECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
http://hdl.handle.net/11449/221281
2-s2.0-85064159132
identifier_str_mv ECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
2-s2.0-85064159132
url http://hdl.handle.net/11449/221281
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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