Bureau of Economic Geology, The University of Texas at Austin (www.beg.utexas.edu).
For more information, please contact the author.
Keynote Address, Lithology and Fluid Prediction Workshop, European Association
of Geoscientists and Engineers (EAGE) Convention, Stavanger, Norway, June
and Fluid Prediction in Exploration: Think Vectors!
For decades, the seismic information that has been used to estimate lithological
and fluid properties of a propagating medium has been data in scalar form.
For example, marine seismic data have been generated with scalar sources
(air guns suspended in water) and recorded with scalar sensors (hydrophones).
Onshore, vector sources (such as vertical-displacement vibrators) and
vector sensors (vertical geophones) have been used, but we still have
extracted only the vertical component of the P wavefield from the data
and then used those data as scalar information. Scalar seismic data and
vector seismic data are significantly different data domains. We will
discuss some of the restrictions we have imposed on the rock/fluid prediction
process by limiting our thought processes to scalar data. For example,
how can we estimate anisotropic parameters of a medium with scalar data?
We must begin thinking of the seismic data that we use in rock/fluid prediction
as vector data. The downgoing near-field wavefields produced by a vector
source in a homogeneous Earth consist of three fundamental modes: a compressional
mode (P) and two shear modes (SH and SV). Each of these modes has a distinct
particle displacement vector. The magnitudes and orientations of these
particle displacement vectors carry valuable rock and fluid information.
This information becomes available only if the vector properties of the
illuminating wavefields are preserved during data acquisition, processing,
and interpretation. In anisotropic media, the orientations of these particle
displacement vectors shift so that they are no longer normal to, or tangent
to, the propagating wavefield. However, these anisotropic wave modes (qP,
qSH, qSV) can still be analyzed as vector data.
Once we use vector-wavefield seismic data to illuminate targets, new options
for lithology and fluid estimation become available. Reflected wavefields
are more complicated than the near-field illumination wavefields because
several types of upgoing wave modes can be produced by the reflection
process at an interface. These additional upgoing modes result from the
fact that P and SV modes are coupled, allowing P displacements to convert
into SV displacements during reflection/transmission. Similarly, SV displacement
can generate P displacement. This transfer of energy from P to SV is the
basis of the popular, scalar-based, P-wave AVO technology that is widely
practiced in rock and fluid prediction.
We will look at a
number of examples where reflected vector-wavefield data provide new insights
into the conditions that exist within a propagation medium. For example,
traditional seismic stratigraphy is based on scalar P-wave data. However,
different seismic sequences and seismic facies become available when vector
data are utilized in seismic stratigraphy analyses. Interpreters now need
to transition from scalar seismic stratigraphy to elastic-wavefield seismic
If we use vector-based seismic information rather than scalar-based seismic
data in rock/fluid predictions, the subsurface geologic and engineering
control used in these estimation efforts needs to be expressed in the
same vector-space coordinates as the seismic data. An important challenge
remains: How should scalar geologic/engineering information be transformed
into vector data to implement a new generation of rock/fluid prediction?
An emerging seismic technology that is beginning to dominate exploration
and exploitation programs is the concept of the vector seismic wavefield.
Seismic sources, sensors, processing algorithms, and interpretation principles
are shifting from the scalar world to the vector world to take advantage
of the new rock/fluid information that is becoming available with vector
data rather than scalar data. Vector thinking must now begin to be applied
in seismic prediction of subsurface rock and fluid properties.