Constraining Geostatistical
Reservoir Models with Seismic
Attributes
By
Richard L. Chambers1, Jeffrey M. Yarus2
(1) Quantitative Geosciences, Inc, Broken Arrow, OK (2) Quantitative Geosciences, Inc, Houston, TX
Complex seismic
trace analysis appeared with the advent of
seismic
sequence
stratigraphy in the mid 1970s. Vail and his colleagues expected that
seismic
attributes would eventually quantify their
seismic
facies parameters. Since then
we have seen a proliferation in the number (hundreds) and often times an
inappropriate use of
seismic
attributes for reservoir characterization. Efforts
to understand the meaning of the plethora of
seismic
attributes include the use
of linear and non-linear techniques, such as Fourier spectral analysis,
Principle Components, Discriminate Function, neural networks, and others. The
idea was that perhaps combinations of attributes might make sense when
individually the attributes lacked clear geological significance, except that
they revealed some sort of pattern. Most of the attributes are highly correlated
simply because they are derivatives of one another and there is no guarantee
that their correlation with a reservoir property is meaningful. Great care must
be taken when choosing
seismic
attributes, because it is not unusual to find
spurious or false correlations that do not reflect any physical basis for the
relationship and the probability of finding false correlations increases with
the number of
seismic
attributes considered and is inversely proportional to the
number of data control points. It is time to return to “first principles” and
establish a clear relationship between a reservoir
attribute
, be it facies,
porosity, or lithology, for example, and a
seismic
attribute
(s).
We illustrate the use of seismic
attributes following a four step procedure:
1) Calibration phase, 2.) Choice of the
seismic
attribute
, 3) Cross-validation,
and 4) Prediction and Uncertainty Analysis using Collocated Cokriging and
Collocated Cosimulation.