Target-oriented data conditioning for prestack inversion in an unconventional reservoir: A Canadian case study

Abstract

Target-oriented data conditioning is a key part of any reservoir characterization workflow. Data conditioning is used to optimize the match between the synthetic data, used in the prestack inversion, and the real data. When this is done correctly, the accuracy and confidence of inversion results may be greatly improved. This is proved on prestack seismic inversion results from a resource play in Canada. The flow is broken down into prestack gather conditioning, which improves the signal-to-noise and gather flatness, and poststack conditioning, which further improves the coherency prior to applying spectral balancing. As a final key step, spatially variant amplitude balancing is used to calibrate the angle stacks to the expected background trend from the well synthetics. The combination of all steps is demonstrated via [Formula: see text] and mu-rho versus lambda-rho crossplots, between the inverted results and the well measurements, to provide a significant improvement on the resolution and accuracy of the final prestack inversion.