2014-09-182014-09-182004-01-27MIRANDA, Anna Ilcéa Fischetti. Imageamento da porosidade através de perfis geofísicos de poço. 2004. 65 f. Tese (Doutorado) - Universidade Federal do Pará, Centro de Geociências, Belém, 2004. Curso de Pós-Graduação em Geofísica.https://repositorio.ufpa.br/handle/2011/5763Porosity 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.porAcesso AbertoPerfilagem geofísica de poçosPorosidadeRedes neurais artificiaisLago Maracaibo - VenezuelaVenezuela - PaísTeseCNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::GEOFISICA::GEOFISICA APLICADA