Teses em Geofísica (Doutorado) - CPGF/IG
URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2357
O Doutorado Acadêmico pertente a o Programa de Pós-Graduação em Geofísica (CPGF) do Instituto de Geociências (IG) da Universidade Federal do Pará (UFPA).
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Navegando Teses em Geofísica (Doutorado) - CPGF/IG por Orientadores "ANDRADE, André José Neves"
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Item Acesso aberto (Open Access) Caracterização de fraturas em imagens de amplitude acústica utilizando morfologia matemática(Universidade Federal do Pará, 2013) XAVIER, Aldenize Ruela; GUERRA, Carlos Eduardo; http://lattes.cnpq.br/7633019987920516; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926Fractures analysis is of particular interest in the characterization of carbonate reservoir since the fractures are the classic geological setting for stock and produce hydrocarbon in this kinds of reservoirs. Particularly in Brazil is growing the interest in the characterization of carbonate reservoirs, with the recent discoveries in pre-salt. The acoustic imaging tools provide valuable information about the amplitude of the reflected waves in the borehole wall, which can be interpreted to allow the characterization of fractures. However, some problems arise due to the qualitative interpretation of these images that are basically performed with the use of vision and experience of the interpreter. This work presents a methodology that performing the fractures analysis of acoustic images and can be divided into three steps. The first one presents the image modeling, which is used to infer the aspect of the fractures in different geological settings. In the second step, the mathematical morphology is used as an edge detector and performs the fractures identification in the acoustic image. The last step deals with the extraction of geometric attributes of the fractures with the adoption of a four degree polynomial according to the least square criterion. The evaluation of this methodology is performed with synthetic images generated by the presented modeling, which supports the characterization of fractures performed in real images.Item Acesso aberto (Open Access) Imageamento da porosidade através de perfis geofísicos de poço(Universidade Federal do Pará, 2004-01-27) MIRANDA, Anna Ilcéa Fischetti; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926Porosity images are graphical representations of the lateral distribution of rock porosity estimated from well log data. We present a methodology to produce this geological image entirely independent of interpreter intervention, with an interpretative algorithm approach, which is based on two types of artificial neural networks. The first is based on neural competitive layer and is constructed to perform an automatic interpretation of the classical Pb - ΦN cross-plot, which produces the log zonation and porosity estimation. The second is a feed-forward neural network with radial basis function designed to perform a spatial data integration, which can be divided in two steps. The first refers to well log correlation and the second produces the estimation of lateral porosity distribution. This methodology should aid the interpreter in defining the reservoir geological model, and, perhaps more importantly, it should help him to efficiently develop strategies for oil or gas field development. The results or porosity images are very similar to conventional geological cross-sections, especially in a depositional setting dominated by clastics, where a color map scaled in porosity units illustrates the porosity distribution and the geometric disposition of geological layers along the section. The methodology is applied over actual well log data from the Lagunillas Formation, in the Lake Maracaibo basin, located in western Venezuela.Item 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/8388930487104926Archie 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.