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We propose a sequential methodology for time-lapse FWI in a Bayesian framework. For this task, we employed the Hamiltonian Monte Carlo method to sampling the posterior distribution. We quantify the uncertainty using synthetic data under different prior choices to the monitor estimation. We have found that the prior information directly impacts the time-lapse results and that uncertainty analysis can provide good criteria to discriminate inversion artefacts from true reservoir changes.