Navegando por Assunto "Lago Maracaibo - Venezuela"

Agora exibindo 1 - 2 de 2

Acesso aberto (Open Access)
Determinação automática da porosidade e zoneamento de perfis através da rede neural artificial competitiva
(Universidade Federal do Pará, 2000) LIMA, Klédson Tomaso Pereira de; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Two of the most important activities of log interpretation, for the evaluation of hydrocarbon reservoirs are the log zonation and the effective porosity calculation of the rocks crossed by the well. The log zonation is the visual log interpretation for the identification, in depth, of the reservoir layers and its vertical limits, that is to say, it is the formal separation in reservoir rocks and non reservoir rocks (shales). The log zonation procedure is accomplished in a manual way, being been worth of the geologic and geophysical knowledge, and of the experience of the log analyst, in the visual evaluation of the curve patterns (log characteristics) corresponding to each specific rock type. The calculation of the effective porosity (porosity of the rock reservoir corrected by clay effects), combines a visual activity so much in the identification of the representative points of a reservoir rock in the log, as well as the adapted choice of the petrophysics equation, that relates the physical properties of the rock to the porosity. Starting from the knowledge of the porosity, the hydrocarbon volume will be established. This activity, essential for the reservoirs qualification, requests a lot of the knowledge and of the experience of the log analyst, for the effective porosity evaluation. An efficient form of automating these procedures and assistant the log analyst, in these activities, that particularly demand a great expenditure of time, is presented in this dissertation, in the form of a new log, derived of the traditional porosity logs, that presents the log zonation, highlighting the top and base depths of the occurrences of reservoir rocks, and non reservoir rocks, scaled in form of effective porosity, called here, as "zoning effective porosity log". The obtaining of the zoning effective porosity log, is based on the project and execution of several architectures of artificial neural feedforward networks, with not supervised training, and contends a layer of artificial competitive neurons. Projected in way to simulate the behavior of the log analyst, when he uses the neutron-density chart, for the situations of applicability of the shale-sandstone model. The applicability and limitations of this methodology will be appraised on real data, originated from of Lago Maracaibo's basin (Venezuela).
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/8388930487104926
Porosity 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.