--> --> Reducing geologic uncertainty in seismic interpretation

AAPG Pacific Section and Rocky Mountain Section Joint Meeting

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Reducing geologic uncertainty in seismic interpretation


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.