| Summary: | Abstract The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface, with applications ranging from subsurface resource exploration to geothermal, carbon capture and storage, and hydrogen storage. We propose a novel, data‐driven approach, named Seis2Rock, that utilizes optimal basis functions learned from well‐log information to create a direct link between pre‐stack seismic data and so‐called band‐limited petrophysical reflectivities. Seis2Rock is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic amplitude variation with offset gathers created from measured or rock‐physics synthesized elastic well logs. In inference, seismic pre‐stack data are first projected into a set of band‐limited petrophysical reflectivities using the previously computed basis functions; this is followed by regularized post‐stack seismic inversion of the individual properties. In this work, we apply the Seis2Rock methodology to a synthetic data set based on the Smeaheia reservoir model and to the open Volve field data set. Our numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time‐lapse, pre‐stack seismic data for water saturation changes.
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