Optimization procedure to minimize FPSO fuel consumption under two operation modes in a Brazilian deep-water oil field
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
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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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 |
|
_version_ |
1808129330295865344 |