--> Petrophysical Characterization of a Clastic Reservoir in the Middle Magdalena Valley Basin in Colombia Using Artificial Neural Networks and Seismic Attributes

AAPG ACE 2018

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Petrophysical Characterization of a Clastic Reservoir in the Middle Magdalena Valley Basin in Colombia Using Artificial Neural Networks and Seismic Attributes

Abstract

We apply instantaneous seismic attributes to a stacked P-wave reflected seismic section in the Tenerife field located in the Middle Magdalena Valley Basin (MMVB) in Colombia to estimate effective porosity, water saturation SW and volume of clay at seismic scale. The well-logs and the seismic attributes associated to the seismic trace closer to one of the available wells is the information used to train some multi-layered Artificial Neural Networks (ANN). We perform data analysis via the Gamma test, a mathematically non-parametric nonlinear smooth modeling tool, to choose the best input combination of seismic attributes to train an artificial neural network (ANN) for estimating porosity, SW and volume of clay. Once the ANN is trained it is applied to predict these parameters along the seismic line. This is a significant result that shows for the first time a petrophysical characterization of this field at seismic scale. From the continuous estimations of volume of clay we distinguish two facies: sands and

clay, these estimations confirm the production of the Mugrosa C-Arenas zone and we draw brown clay that correlate with the high amplitude attributes and the yellow sand correlate with the low amplitude attributes.