We applied the seismic net-pay (SNP) method to an oil discovery and predicted thicknesses consistent with the actual thicknesses at the wellbore locations. This was accomplished by applying the method in a self-calibrating mode that did not require the direct use of well information. For net-pay estimation under a self-calibration scenario, the SNP method thickness estimates proved to be more accurate (mean absolute prediction error at well validation locations under 3.0±1.5m) than estimates from a reflectivity-based detuning method (4.0±2.7m) or multiple linear regression (5.9±5.8m). Statistical F-tests indicated that the correspondences of the predicted thickness estimates with actual net-pay values for the SNP and reflectivity methods (F approximately 5.5–6 for both) were statistically significant, whereas the multiple regression results did not prove to be statistically significant.

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