I propose to use the comprehensive 3-D Amoco data set to estimate reservoir mechanical properties and their spatial variation in a true 3-D sense. This procedure will require sequential data processing steps involving: (1) P and S velocity analysis from 3-D and 2-D reflection data, (2) 3-D prestack migration images of reservoir structure, stratigraphy and fault geometry, (3) 3-D simultaneous prestack inversion of the and reflection data for P and S relative impedance contrasts within the reservoir zone, and (4) integration of the P and S velocity and impedance contrast information to make estimates of the P and S impedances IP and IS, in a 3-D spatial volume encompassing the reservoir block.
The most significant portion of this research effort is directed at the problem of estimating impedance contrasts from seismic reflection data. I will approach this problem in the following manner. Using a reliable estimate of the P and S migration velocity models resulting from the velocity analysis step (1), multiple constant offset elastic migrations will be performed for each of the and data sets. This procedure will result in four sequences of maps: ,, and , where R represents the angle-dependent elastic reflectivity estimate, and represents the reflection angle estimate. The R and values make a direct mapping to and reflectivity as a function of reflection angle, which can subsequently be inverted for P and S impedance contrasts, using the Zoeppritz plane-wave response, as a function of subsurface position. The theory for making these reflectivity and angle estimates will be based on elastic Kirchhoff integrals and ray-theoretic WKBJ Green's functions (Lumley and Beydoun, 1991; and Lumley 1992, 1993a and 1993b.)
Once the impedance contrasts are estimated, they can be linearly combined with the P and S velocity estimates to make broadband impedance estimates. Spatial variations in IP and IS are useful in that they have physical correlations with: presence and saturation of gas in pore space (IP low, IS normal to high), fractured high permeability zones (both IP and IS are low), and can be combined to estimate 3-D variations in Poisson's Ratio, which can be a good discriminator of lithology (e.g., sandstone vs. shale vs. coal). I propose to correlate my IP and IS results with Amoco borehole, rock physics, and production engineering data. This correlation will be at the very least a qualitative integration of these multi-disciplinary data types, and may possibly extended to a quantitative integration using existing Markov-Bayes methods of spatial geostatistics.