GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1115/OMAE2022-78612 http://hdl.handle.net/11449/247805 |
Resumo: | Cost optimization of asset management is a central issue in the offshore Oil and Gas strategy, and risk-based approaches, such as Risk Based Inspection (RBI), have been more and more employed to assist the segment in the concretization of that goal. The inspection procedure aims mitigating the uncertainty related to the asset degradation state, enabling a better quantification of the actual damage, and, consequently, increases the accuracy of remaining life projections. Since the costs involved in offshore subsea inspections are considerable, inspection plans optimization plays a crucial role in the balance of an asset management program. The present paper discusses the use of genetic algorithms, GA, an optimization technique inspired in the concepts of evolutionary genetics, in the development of inspection plans for subsea equipment. The genes are defined in terms of two variables: the type of inspection to be done and in which period it occurs, considering a finite window of opportunities for inspection occurrences. The optimization process considers a multicriteria objective function, consisting in the dimensions time, cost, and risk. Finally, the application of the proposed methodology is illustrated by means of the elaboration of the inspection plans for a subsea Christmas-tree. |
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GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANSGenetic AlgorithmsMulticriteria Objective FunctionNon-Dominated Sorting Genetic Algorithm II (NSGA-II)OffshoreOil and GasRisk Based Inspection (RBI)Cost optimization of asset management is a central issue in the offshore Oil and Gas strategy, and risk-based approaches, such as Risk Based Inspection (RBI), have been more and more employed to assist the segment in the concretization of that goal. The inspection procedure aims mitigating the uncertainty related to the asset degradation state, enabling a better quantification of the actual damage, and, consequently, increases the accuracy of remaining life projections. Since the costs involved in offshore subsea inspections are considerable, inspection plans optimization plays a crucial role in the balance of an asset management program. The present paper discusses the use of genetic algorithms, GA, an optimization technique inspired in the concepts of evolutionary genetics, in the development of inspection plans for subsea equipment. The genes are defined in terms of two variables: the type of inspection to be done and in which period it occurs, considering a finite window of opportunities for inspection occurrences. The optimization process considers a multicriteria objective function, consisting in the dimensions time, cost, and risk. Finally, the application of the proposed methodology is illustrated by means of the elaboration of the inspection plans for a subsea Christmas-tree.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Analysis Evaluation and Risk Management Laboratory (LabRisco) Naval Architecture and Ocean Engineering Department University of São PauloResearch and Development Center CENPES - PetrobrasInstitute of Science and Engineering (ICE) Sao Paulo State University (Unesp) Evaluation and Risk Management Laboratory (LabRisco), Campus of Itapeva AnalysisInstitute of Science and Engineering (ICE) Sao Paulo State University (Unesp) Evaluation and Risk Management Laboratory (LabRisco), Campus of Itapeva AnalysisCNPq: 308712/2019-6Universidade de São Paulo (USP)CENPES - PetrobrasUniversidade Estadual Paulista (UNESP)Morais, Carlos Henrique Bittencourtde Moura, Fernanda MarquesAbrahão, ElcioMaturana, Marcos CoelhoMartins, Marcelo Ramosde Barros, Leonardo OliveiraOrlowski, Rene Thiago CapelariRossi, André Luis Debiaso [UNESP]Schleder, Adriana Miralles [UNESP]2023-07-29T13:26:22Z2023-07-29T13:26:22Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1115/OMAE2022-78612Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, v. 2.http://hdl.handle.net/11449/24780510.1115/OMAE2022-786122-s2.0-85140791224Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAEinfo:eu-repo/semantics/openAccess2024-11-22T13:49:10Zoai:repositorio.unesp.br:11449/247805Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-03-28T14:50:07.600376Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
title |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
spellingShingle |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS Morais, Carlos Henrique Bittencourt Genetic Algorithms Multicriteria Objective Function Non-Dominated Sorting Genetic Algorithm II (NSGA-II) Offshore Oil and Gas Risk Based Inspection (RBI) |
title_short |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
title_full |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
title_fullStr |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
title_full_unstemmed |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
title_sort |
GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS |
author |
Morais, Carlos Henrique Bittencourt |
author_facet |
Morais, Carlos Henrique Bittencourt de Moura, Fernanda Marques Abrahão, Elcio Maturana, Marcos Coelho Martins, Marcelo Ramos de Barros, Leonardo Oliveira Orlowski, Rene Thiago Capelari Rossi, André Luis Debiaso [UNESP] Schleder, Adriana Miralles [UNESP] |
author_role |
author |
author2 |
de Moura, Fernanda Marques Abrahão, Elcio Maturana, Marcos Coelho Martins, Marcelo Ramos de Barros, Leonardo Oliveira Orlowski, Rene Thiago Capelari Rossi, André Luis Debiaso [UNESP] Schleder, Adriana Miralles [UNESP] |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) CENPES - Petrobras Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Morais, Carlos Henrique Bittencourt de Moura, Fernanda Marques Abrahão, Elcio Maturana, Marcos Coelho Martins, Marcelo Ramos de Barros, Leonardo Oliveira Orlowski, Rene Thiago Capelari Rossi, André Luis Debiaso [UNESP] Schleder, Adriana Miralles [UNESP] |
dc.subject.por.fl_str_mv |
Genetic Algorithms Multicriteria Objective Function Non-Dominated Sorting Genetic Algorithm II (NSGA-II) Offshore Oil and Gas Risk Based Inspection (RBI) |
topic |
Genetic Algorithms Multicriteria Objective Function Non-Dominated Sorting Genetic Algorithm II (NSGA-II) Offshore Oil and Gas Risk Based Inspection (RBI) |
description |
Cost optimization of asset management is a central issue in the offshore Oil and Gas strategy, and risk-based approaches, such as Risk Based Inspection (RBI), have been more and more employed to assist the segment in the concretization of that goal. The inspection procedure aims mitigating the uncertainty related to the asset degradation state, enabling a better quantification of the actual damage, and, consequently, increases the accuracy of remaining life projections. Since the costs involved in offshore subsea inspections are considerable, inspection plans optimization plays a crucial role in the balance of an asset management program. The present paper discusses the use of genetic algorithms, GA, an optimization technique inspired in the concepts of evolutionary genetics, in the development of inspection plans for subsea equipment. The genes are defined in terms of two variables: the type of inspection to be done and in which period it occurs, considering a finite window of opportunities for inspection occurrences. The optimization process considers a multicriteria objective function, consisting in the dimensions time, cost, and risk. Finally, the application of the proposed methodology is illustrated by means of the elaboration of the inspection plans for a subsea Christmas-tree. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-07-29T13:26:22Z 2023-07-29T13:26:22Z |
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 |
http://dx.doi.org/10.1115/OMAE2022-78612 Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, v. 2. http://hdl.handle.net/11449/247805 10.1115/OMAE2022-78612 2-s2.0-85140791224 |
url |
http://dx.doi.org/10.1115/OMAE2022-78612 http://hdl.handle.net/11449/247805 |
identifier_str_mv |
Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, v. 2. 10.1115/OMAE2022-78612 2-s2.0-85140791224 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE |
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) |
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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 |
repositoriounesp@unesp.br |
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1834483036864905216 |