Data-driven prestack-waveform inversion using genetic algorithm: Methodology and examples

Abstract

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data-driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizontal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudowells by running a two-pass 1D isotropic elastic prestack-waveform inversion that uses the reflectivity method for forward modeling and the genetic algorithm for optimization. We then use the estimated pseudowells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geologic settings, we determine the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is, however, necessary to improve the computational efficiency of the methodology.