A fuzzy-logic approach accurately characterizes spatial porosity and permeability for a complex, naturally fractured carbonate reservoir. The reservoir lies in the Cretaceous section alongside the fault damage and shear zone at the downthrown block to the west of the Icotea fault, Central North segment of the Maracaibo basin.

To identify the pore type in our characterization of this carbonate reservoir, we first used fuzzy logic to obtain fracture indexes from conventional well logs. Then we used a resistivity/porosity model with cementation factor, m, variable to determine fracture porosity, and calculated total porosity from neutron-density logs. After that, we developed a neuro-fuzzy logic permeability model with porosity, shale volume, fracture indexes, and pore type as independent variables.

Next, we used directional-statistics tool analysis to identify the five main fracture families and categorize them. The diagnosis of fracture orientation relies on the statistical analysis of fractures from four wells, three of them having image logs and one having a direct description of fractures from an oriented core. We also compared the fracture-orientation data from well data with the faults interpreted at seismic scale to unravel their structural connection.

Fracture lengths are obtained from curvature maps by direct measurement of lineaments matching the azimuth of each family. A logarithmic fit of the known values allows extrapolating values of fracture lengths and spacings below seismic resolution. To propagate porosity in our fracture system, where only a seismic P-wave volume was available, we found for each fracture family a general inverse functional relationship between fracture intensity and fracture porosity, introduced as the r-pi method of correlation.

With a sigmoidal function we correlate the neuro-fuzzy logic permeability variable with the aperture and intensity of the fractures. With a statistically based characterization we identify wheter the fractures display directionally or stratigraphically related anisotropies. Finally, to populate petrophysical properties in the fracture network we apply the functions devised starting with the size of the fractures as an independent variable.

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