Navegando por Autor "SILVA, Carolina Barros da"

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Acesso aberto (Open Access)
Interpretação do gráfico de Hingle através de rede competitiva angular
(Universidade Federal do Pará, 2007) SILVA, Carolina Barros da; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Water saturation is an important petrophysical property for formation evaluation, defining the final wellbore destination. The Archie’s equation calculates the water saturation for clean formations in function of rock resistivity, from a deep resistivity log and porosity, from one porosity log. The Archie’s equation, still involves the knowledge of formation water resistivity, which requires local determination and appropriated Archie’s coefficients. Hingle plot is traditional method in well logging for water saturation calculus, specially when the water resistivity is unknown. This method promotes a linearization of Archie’s equation from resistivity and porosity logs as the water line in the Hingle plot. The water resistivity is obtained from water line inclination. Independent of logging tools and digital computers development, the log analyst still handles with visual data interpretation and as all visual data interpretation, the Hingle plots interpretation is subject of sharpness errors. The objective of this dissertation is to simulate the visual interpretation of Hingle plot by a angular competitive neural network to mitigate the occurrence of sharpness errors and produces a real time first approach of water saturation, based on angular pattern identification in the raw well logging data.. The evaluation of this methodology is accomplished on synthetic data that satisfies the Archie’s equation and on actual well logging data.
Acesso aberto (Open Access)
Solução da equação de Archie com algoritmos inteligentes
(Universidade Federal do Pará, 2011) SILVA, Carolina Barros da; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Archie equation is a historical mark of Formation Evaluation establishing a relationship among the physical properties and the petrophysical properties of reservoir rocks, which makes possible the identification and quantification of hydrocarbon in subsurface. Water saturation is the solution of Archie equation obtained from the measure of formation deep resistivity and porosity estimated. However, the solution of Archie equation is no trivial, in the dependence of previous knowledge of formation water resistivity and Archie exponents (cementation and saturation). This thesis introduces a set new intelligent algorithm to solve Archie equation. A modification of competitive neural network, nominated as bicompetitive neural network produces the log zonation. A new genetic algorithm with evolutionary strategy based in the mushrooms reproduction produces estimates for the matrix density, the matrix transit time and the matrix neutron porosity, which associated to a new rock model, produces realistic porosity estimates considering shale effects. A new model of competitive neural network, nominated as angular competitive neural network is able to accomplish the interpretation of Pickett plot, supplying the information about formation water resistivity and cementation exponent. All results of the methodology hereintroduced are presented using synthetic data and actual wireline logs and core analysis results.