Uma contribuição à análise de decisão sobre a exploração comercial de poços de petróleo offShore: um sistema fuzzy para inferir a concentração de bactérias redutoras de sulfato em suporte ao processo de análise de amostras de óleo

• Ano de defesa: 2018
• Autor(a) principal: Nacif Junior, Luiz Cezar
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia de Produção; UFRJ
• Palavras-chave em Português: Robótica; Lógica fuzzy; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA DO PRODUTO
• Link de acesso: http://hdl.handle.net/11422/12209

The aim of this thesis is to contribute to the feasibility studies for the commercial exploitation of OffShore oil wells. A Fuzzy approach, using a Mamdani inference system, treats four primary factors that characterize petroleum samples (namely: API Grade, Sample Dilution, Sulfur Content and pH of the Hydrogen Sulphide in the Medium) to determine the concentration of Sulphate Reducing Bacteria (SRB), information element of interest of this thesis. This treatment involves the emulation of a fuzzy optical sensor that identifies the color of the sample, allowing the definition of the correlated SRB concentration, contributing to the commercial viability analysis of the well. This approach arose from the identified need to circumvent the situation of delays (usually 28 days) and inefficiency of human resources allocation (dedication of a full time professional), which characterize the current processes and practices used in the analysis of the sample. In this way, the proposal constitutes an original contribution that increases the reliability of the analysis process and facilitates data collection and its transformation into information for decision making during the incubation of the Sulphate Reduction Bacteria (SRB), using the methodology of image pattern recognition and associated robotics, both by fuzzy logic.