Abstract
Prestack seismic waveform inversion starts with an initial model and computes synthetic or predicted seismic data using a wave equation-based approach. Then, by matching these predicted data with the observed seismic data, it iteratively modifies the initial model using an optimization method until the predicted and observed data reasonably match. This method has been demonstrated to be superior to amplitude-variation-with-angle inversion. Because of the wave equation-based approach, computational cost is, however, one major drawback of the method. In the presence of well-logs with borehole measurements of the subsurface properties such as the P-wave velocity, S-wave velocity, and density, it is possible to provide a good initial model, and the method quickly converges to the true model at well locations. However, for locations away from the wells, the initial models are obtained by interpolating the initial models at the well locations over the interpreted geological horizons. These models can be far from the true models and inverting prestack data for these locations using wave equation-based method is computationally challenging. Because of these computational challenges, amplitude-variation-with-angle inversion is the current state-of-the-art method for routine seismic inversion applications. In this work, we provide an attribute-guided framework to generate initial models and demonstrate its applicability, which can potentially overcome computational challenges of prestack seismic waveform inversion. Furthermore, we also discuss the feasibility of using this attribute-guided approach to characterize reservoirs for underground hydrogen storage. Keywords: seismic inversion; attribute-guided; underground hydrogen storage
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