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.
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