LITHOLOGY AND FLUID SEISMIC DETERMINATION FOR THE ACAE AREA, PUERTO COLÓN OIL FIELD, COLOMBIA

Abstract

In this work, we applied enhanced geophysical techniques to detect new prospecting zones at the Puerto Colón oil field. The easier-to-produce hydrocarbons are currently being or have been extracted. In order to extract harder-to-produce hydrocarbons, we need to better define the Caballos formation characteristics.  We obtained an acceptable match between the rock-physics laboratory measurements and the petrophysical properties estimated through the use of seismic data.  We used well logs to guide the seismic measurements in the estimation of both porosity and gamma-ray response (from seismic attributes), and acoustic impedance (via seismic inversion), using a neural network approach.  We applied a probabilistic neural network (PNN) because of its particular characteristics of 1) mapping non linear relationships between seismic and well log data; 2) incrementing both accuracy and resolution when performing inversion, as compared to conventional inversion, and; 3) using a mathematical interpolation scheme not implemented as a black box. Poisson and Vp/Vs ratio provide a means to discriminate between high and low reservoir-rock quality at the Caballos formation. Finally, we analyzed three angle gather stacks (0°-10°, 11°-20° and 21°-30°) through elastic inversion.