  4D seismic inversion is the leading method to quantitatively monitor fluid
flow dynamics in the subsurface, with applications ranging from enhanced oil
recovery to subsurface CO2 storage. The process of inverting seismic data for
reservoir properties is, however, a notoriously ill-posed inverse problem due
to the band-limited and noisy nature of seismic data. This comes with
additional challenges for 4D applications, given inaccuracies in the
repeatability of the time-lapse acquisition surveys. Consequently, adding prior
information to the inversion process in the form of properly crafted
regularization terms is essential to obtain geologically meaningful subsurface
models. Motivated by recent advances in the field of convex optimization, we
propose a joint inversion-segmentation algorithm for 4D seismic inversion,
which integrates Total-Variation and segmentation priors as a way to counteract
the missing frequencies and noise present in 4D seismic data. The proposed
inversion framework is applied to a pair of surveys from the open Sleipner 4D
Seismic Dataset. Our method presents three main advantages over
state-of-the-art least-squares inversion methods: 1. it produces
high-resolution baseline and monitor acoustic models, 2. by leveraging
similarities between multiple data, it mitigates the non-repeatable noise and
better highlights the real time-lapse changes, and 3. it provides a volumetric
classification of the acoustic impedance 4D difference model (time-lapse
changes) based on user-defined classes. Such advantages may enable more robust
stratigraphic and quantitative 4D seismic interpretation and provide more
accurate inputs for dynamic reservoir simulations. Alongside our novel
inversion method, in this work, we introduce a streamlined data pre-processing
sequence for the 4D Sleipner post-stack seismic dataset, which includes
time-shift estimation and well-to-seismic tie.
