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

Abstract

We apply instantaneous Previous HitseismicNext Hit attributes to a stacked P-wave reflected Previous HitseismicNext Hit 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 Previous HitseismicNext Hit scale. The well-logs and the Previous HitseismicNext Hit attributes associated to the Previous HitseismicNext Hit 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 Previous HitseismicNext Hit 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 Previous HitseismicNext Hit line. This is a significant result that shows for the first time a petrophysical characterization of this field at Previous HitseismicTop 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.