Waveform Gather Inversion and Attribute-Guided Interpolation: A Two-Step Approach to Log Prediction
By
August Lau1, Alfonso Gonzalez2, Subhashis Mallick2, Diana Gillespie2
(1) Apache Corporation, Houston, TX (2) WesternGeco, Houston, TX
We present a two-step approach to predict log information. In the first step
nonlinear waveform gather inversion is used to estimate VP, VS, and density from
the full seismic
gathers. This estimation is useful in areas with little or no
well control, as well as in mature basins where log information might be
available but might not be complete. Waveform gather inversion is
computationally intensive, and therefore, difficult to apply to every gather in
a
seismic
volume. In the second step,
seismic
attributes are used to guide the
interpolation of the predicted logs for the entire
seismic
volume. Interpolation
of log properties can be done in several ways. A traditional way is to use
hybrid inversion, elastic impedance inversion, or poststack amplitude inversion.
These methodologies depend exclusively on amplitude information and use no other
attributes. This traditional inversion is also strongly dependent on the
interpretation
of horizons. The
interpretation
in certain areas could be
challenging, as in the following types of geologic settings: carbonate buildup,
channel complex, crossing fault, angular unconformity, or turbidite.
Furthermore, certain poststack inversions might be too sensitive to the
interpretation
, which is not always desirable. Neural network interpolation does
not require horizon
interpretation
. Our two-step approach overcomes the high
cost of waveform gather inversion, and results in a parallelized workflow where
the inversion and neural network interpolation are done independently of the
structural
interpretation
of the
seismic
data
. This overcomes the bottleneck of
a linear workflow of processing,
interpretation
, and inversion.