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The AAPG/Datapages Combined Publications Database
Houston Geological Society Bulletin
Abstract
Abstract: Using
Seismic
Attributes to Predict
Reservoir Properties-Potential Risks
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By
Kalkulations, Inc.
Advances in software for generating seismic
attributes and
the growing emphasis on production geophysics have led to
the widespread use of
seismic
attributes as predictors of reservoir
properties. Often, we can show-using
seismic
modeling or
from rock physics-a physically justifiable relationship between
a
seismic
attribute
and the reservoir property of interest. When
this is true, we can reduce greatly the uncertainty of interwell
predictions of reservoir properties.
The first critically important step in the use of seismic
attributes is to accurately tie the well and
seismic
data-both
vertically and aerially. Successful application then relies on identifying
a
seismic
attribute
that is significantly correlated with the
reservoir property being modeled. If found, the dense
seismic
data can be used to guide the interpolation between sparse well
data using geostatistical, regression, or neural network techniques.
The purpose of this process is to estimate volumes of
petroleum in place or to make reservoir management decisions
such as the location and number of wells, depletion strategy, or
gas and water injection operations.
All of the prediction methods-regression, geostatistics,
and neural networks-require the interpreter to make inferences
from a small number of wells to a larger population assumed to
be represented by that sample. Inference should not be based
solely on an empirical relationship between a seismic
attribute
and reservoir property derived from the small sample. This study
quantifies the probability of observing spuriously high correlations
between the well and
seismic
data; that is, the probability
of observing a significant sample correlation when the
seismic
attributes are actually uncorrelated with the reservoir property.
If the correlation is indeed spurious and the
seismic
attribute
is
used as a predictor, not only will the estimated reservoir property
be biased, but its error variance will be underestimated. This can
lead to highly confident, but very inaccurate, predictions and,
ultimately, to poor reservoir management decisions.
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