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The inversion of petrophysical parameters from seismic data represents afundamental step in the process of characterizing the subsurface. We propose anovel, data-driven approach named Seis2Rock that utilizes optimal basisfunctions learned from well log information to directly link band-limitedpetrophysical reflectivities to pre-stack seismic data. Seis2Rock is composedof two stages: training and inference. During training, a set of optimal basisfunctions are identified by performing singular value decomposition on one ormore synthetic AVO gathers created from measured or rock-physics synthesizedelastic well-logs. In inference, seismic pre-stack data are first projectedinto a set of band-limited petrophysical properties using the previouslycomputed basis functions; this is followed by regularized post-stack seismicinversion of the individual properties. In this work, we apply the Seis2Rockmethodology to a synthetic dataset based on the Smeaheia reservoir model andthe open Volve field dataset. Numerical results reveal the ability of theproposed method in recovering accurate porosity, shale content, and watersaturation models. Finally, the proposed methodology is applied in the contextof reservoir monitoring to invert time-lapse, pre-stack seismic data for watersaturation changes.

Seis2Rock: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data