We have applied instantaneous seismic attributes to a stacked P-wave reflected seismic section in the Tenerife field located in the Middle Magdalena Valley Basin in Colombia to estimate the volume of clay [Formula: see text] and the density [Formula: see text] at seismic scale. The well logs and the seismic attributes associated to the seismic trace closer to one of the available wells (Tenerife-2) is the information used to train some multilayered artificial neural networks (ANN). We perform data analysis via the gamma test, a mathematically nonparametric nonlinear smooth modeling tool, to choose the best input combination of seismic attributes to train ANNs to estimate [Formula: see text] and [Formula: see text]. Once the ANNs are trained, they are applied to predict these parameters along the seismic line. From the continuous estimations of [Formula: see text], we distinguish two facies: sands for [Formula: see text] and shales when [Formula: see text]. These estimations confirm the production of the Mugrosa C-Sands zone, and we draw the brown shale that correlates with the high-amplitude attributes and the yellow sand that correlates with the low-amplitude attributes. Using the well-log information for [Formula: see text] and the facies classification (also in the well log), two cubic polynomials that depend on time (or depth) are obtained, one for sands and the other for shales, to fit the [Formula: see text]. These two cubic polynomials and the facies classification obtained from the [Formula: see text] at the seismic scale enable us to estimate [Formula: see text] at the seismic scale. To validate the 2D [Formula: see text] and [Formula: see text] predicted data, a forward-modeling software (the Kennett reflectivity algorithm) is used. This model calculates synthetic seismograms that are compared with the real seismograms. This comparison indicates a small misfit that suggests that the [Formula: see text] and [Formula: see text] images are representing the reservoir description characteristics and the ANN method is accurate to map these parameters.
Prediction of the water saturation around wells with bottom water drive using artificial neural networks