Detalhes bibliográficos
| Ano de defesa: |
2010 |
| Autor(a) principal: |
Oliveira, Amanda Gondim de |
| Orientador(a): |
Melo, Jorge Dantas de |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Rio Grande do Norte
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Ciência e Engenharia do Petróleo
|
| Departamento: |
Pesquisa e Desenvolvimento em Ciência e Engenharia de Petróleo
|
| País: |
BR
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
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| Link de acesso: |
https://repositorio.ufrn.br/jspui/handle/123456789/12907
|
Resumo: |
The objective of reservoir engineering is to manage fields of oil production in order to maximize the production of hydrocarbons according to economic and physical restrictions. The deciding of a production strategy is a complex activity involving several variables in the process. Thus, a smart system, which assists in the optimization of the options for developing of the field, is very useful in day-to-day of reservoir engineers. This paper proposes the development of an intelligent system to aid decision making, regarding the optimization of strategies of production in oil fields. The intelligence of this system will be implemented through the use of the technique of reinforcement learning, which is presented as a powerful tool in problems of multi-stage decision. The proposed system will allow the specialist to obtain, in time, a great alternative (or near-optimal) for the development of an oil field known |
