Reducing geologic uncertainty in
seismic
interpretation
Abstract
When working with seismic
and petrophysical data, both data types may pose a number of unique challenges to the interpreter. Although 3D
seismic
data provides wide data coverage, the information often lacks granularity and is not a direct measurement of the reservoir properties we are typically interested in obtaining. Petrophysical log data is quite nearly the opposite; in that, we may discern minor changes in reservoir properties along a well bore, but these measurements do not extend more than a few meters away from the logging tool. Combining the best of both data types, geologic models capable of filling in the gaps between
seismic
and petrophysical data sets have become exceeding valuable. This presentation will examine a number of uncertainty reducing workflows associated with both forward and inverse
modeling
techniques. Geophysical forward
modeling
techniques calculate a specific geophysical response given a well-defined physical property model. In the case of 2D
seismic
modeling
, the physical property model can be taken directly from petrophysical log data, sonic and density logs that have been adequately tied to an existing
seismic
survey. Using both available log data combined with geologically reasonable model constraints, geo-modelers may construct a number of modeled
seismic
responses that can be used to help validate or invalidate various working geologic models. In contrast, geophysical inverse
modeling
techniques attempt to construct a physical property model based off of a geophysical response. In the case of
seismic
inversion, impedance values are calculated from an existing
seismic
data set. The largest challenge associated with inverse
modeling
is that there are multiple solutions available given an individual
seismic
data set. By using a simulated annealing (SA) inversion algorithm, geoscientists are able to greatly reduce the total number of possible solutions that are available by leveraging both a background model combined with efficient wavelet estimation for optimal tuning parameters.
AAPG Datapages/Search and Discovery Article #90266 © 2016 AAPG Pacific Section and Rocky Mountain Section Joint Meeting, Las Vegas, Nevada, October 2-5, 2016