DFA e análise de agrupamento aplicadas a perfis de porosidade neutrônico em poços de petróleo

• Ano de defesa: 2009
• Autor(a) principal: Silva, Francisco Wilton de Freitas
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Ciência e Engenharia do Petróleo; Pesquisa e Desenvolvimento em Ciência e Engenharia de Petróleo
• Palavras-chave em Português: Campo de Namorado; Petróleo; Porosidade Neutrônica; DFA; Análise de Agrupamentos; Oil; Neutron Porosity; Cluster analysis; CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA::TECNOLOGIA QUIMICA::PETROLEO E PETROQUIMICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/18534

Peng was the first to work with the Technical DFA (Detrended Fluctuation Analysis), a tool capable of detecting auto-long-range correlation in time series with non-stationary. In this study, the technique of DFA is used to obtain the Hurst exponent (H) profile of the electric neutron porosity of the 52 oil wells in Namorado Field, located in the Campos Basin -Brazil. The purpose is to know if the Hurst exponent can be used to characterize spatial distribution of wells. Thus, we verify that the wells that have close values of H are spatially close together. In this work we used the method of hierarchical clustering and non-hierarchical clustering method (the k-mean method). Then compare the two methods to see which of the two provides the best result. From this, was the parameter � (index neighborhood) which checks whether a data set generated by the k- average method, or at random, so in fact spatial patterns. High values of � indicate that the data are aggregated, while low values of � indicate that the data are scattered (no spatial correlation). Using the Monte Carlo method showed that combined data show a random distribution of � below the empirical value. So the empirical evidence of H obtained from 52 wells are grouped geographically. By passing the data of standard curves with the results obtained by the k-mean, confirming that it is effective to correlate well in spatial distribution