Traditionally static reservoir models are obtained as a solution of an inverse problem where reservoir properties, such as porosity and lithology, are estimated from seismic data. With the emergence of time-lapse reservoir models, we can integrate static and dynamic reservoir properties in the seismic reservoir characterization workflow. Here, we propose a methodology to jointly estimate rock properties, such as porosity, and dynamic property changes, such as pressure and saturation changes, from time-lapse seismic data. This methodology is based on a full Bayesian approach to seismic inversion and can be divided into two steps. First we estimate the conditional probability of elastic properties and their relative changes, then we estimate the posterior probability of static rock properties and dynamic property changes. We applied the proposed methodology to a synthetic reservoir study where we created a synthetic seismic survey for a real dynamic reservoir model including pre-production and production scenarios. The final result is then a set of point-wise probability distributions that allow us to predict the most probable reservoir models at each time step and to evaluate the associated uncertainty.