TY  - JOUR
T1  - Petrophysical Characterization of a Clastic Oil Reservoir in the Middle Magdalena Valley
Basin in Colombia using Artificial Neural Networks, Seismic Attributes, Well Logs and Rock
Physics
AU - Iturrarán-Viveros, Ursula AU - Muñoz-García, Andrés AU - Duque Gómez, Luis Fernando AU - Espitia Nery, Martin Eduardo 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 13
SP  - 2728
EP  - 2733
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.2728.2733
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.2728.2733
KW  - ANN
KW  -MMVB
KW  -parameters
KW  -Gamma test
KW  -C-Sands
AB  - We train Artificial Neural Networks (ANN) to
estimate the seismic scale of the rock parameters in the
lithological units of petroleum interest in Colombia. 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 (ρe), water
saturation (S<sub>W</sub>), density (&#961;) and volume of clay (V<sub>clay</sub>) at
seismic scale. To compute &#961;e, we use Raymerver equation
using the standard parameters for sand formations. The
water saturation (S<sub>w</sub>) was computed using Simandoux
equation for sandstones when clays do not have high
cationic interchanges such as in the Tenerife field. 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, density,
SW and volume of clay. Once the ANN&#146;s are trained
these are 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 shales, these
estimations confirm the production of the Mugrosa
C-Sands zone and we draw brown clay that correlate with
the high amplitude attributes and the yellow sand
correlate with the low amplitude attributes.
ER  -