  The inversion of petrophysical parameters from seismic data represents a
fundamental step in the process of characterizing the subsurface. We propose a
novel, data-driven approach named Seis2Rock that utilizes optimal basis
functions learned from well log information to directly link band-limited
petrophysical reflectivities to pre-stack seismic data. 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 AVO 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 properties 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 dataset based on the Smeaheia reservoir model and
the open Volve field dataset. 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.
