Despite routine demand from petroleum explorationists and field developers, interpreting (inverting) seismic data for reservoir thickness from acoustic impedance (AI) or lithology volume requires a high-quality, unbiased well database and the special skills of elite geophysicists. I have developed a new method, based on linear combination and color blending of multiple-frequency panels, to estimate AI and thickness without the strict implementation of complex mathematics and extensive well control. Aimed at readjusting the thin-bed tuning effect in a formation of normal thickness range (up to λ; λ = dominant wavelength), a linear combination of three frequency panels from 90° data would lead to a reasonable visual match between a sandstone (shale) body and its seismic event, should the combined amplitude spectrum roughly match the AI spectrum. A red-green-blue blending of frequency panels further extends the interpretive benefits by illustrating the thickness in color, adding a sense of thickness cyclicity on the vertical view and that of sandstone thickness map on stratal-slice view. Tests using a simple wedge model and a complex, geologically realistic multi-thin-bed model demonstrate that the proposed workflow may achieve decent geometry (thickness) estimation and reasonably high correlation (r=0.60.7) for AI prediction with minimal or no well control. The results are similar to colored inversion in the fast-track principle, with improved stability and less error (at least in this study). More complex procedures — such as linear regression and model-based inversion — may lead to minor to moderate improvement with adequate well control. An application to a field data set confirmed the value of the methods in high-resolution reservoir-thickness imaging, with a strong potential for stratigraphically oriented studies, such as seismic chronostratigraphy, sequence stratigraphy, and seismic sedimentology.

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