Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as
acoustic impedance, from measured seismic amplitude variations due to hydrocarbon
production. Possible approaches for 4D seismic inversion include two classes of method:
sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare
the standard deterministic methods, such as coloured and model-based inversions, and the
probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the
sequential independent 3D seismic inversions and the joint 4D inversion using the same type of
algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in
time-lapse studies. The model property of interest is the ratio of the acoustic impedances,
estimated for the monitor, and base surveys at each location in the model. We apply the methods
to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using
this controlled dataset, we can evaluate properly the results as the true solution is known. The
results show that the Bayesian 4D joint inversion, based on the amplitude difference between
seismic surveys, provides more accurate results than sequential independent 3D inversion
approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint
inversion is relatively easy to apply and provides a confidence interval of the predictions.