| Summary: | There are a lot of methods to interpret seismic post-stack data, such as
acoustic impedance inversion and multi-attribute method. Both methods are to
integrate between seismic and well data.
Acoustic impedance inversion method is used to predict physical
properties of rocks, specifically the acoustic impedance values. Model based
inversion is one of the most popular inversion techniques. By using the logs
values from the wells, the initial model is made and then be analyzed with the real
seismic data in order to obtain the minimum error prediction and the best
correlation between the initial model and the real seismic data. On the other hand,
multi-attribute method is employed to predict the well-log properties from seismic
data. The log properties that will be predicted are porosity logs. The objective of
this method is to derive a multi-attribute transform, which is a linear or nonlinear
transformation, between the attributes from seismic data and the target log values
from wells. Linear transformation is derived by least-squares minimization,
whereas the nonlinear transformation is derived by train the neural networks,
especially the Probabilistic Neural Networks (PNN).
Both methods are applied to seismic data set in block F3 Netherlands, and
the result of acoustic impedance values are about 4700-5600 (m/s)(gr/cc) and the
porosity values range from 0.27 � 0.32 fraction for shaly sand 1, whereas for
shaly sand 2, the acoustic impedance values are about 3600 � 4500 (m/s)(gr/cc)
and the porosity values are 0.34 � 0.37 fraction.
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