Quantitative integration of spatial and temporal information provided by time-lapse (4D) seismic surveys to dynamic reservoir models calls for an efficient and effective workflow. To solve this issue, we propose a novel workflow which uses a Bayesian/MCMC approach and experimental design-based proxies for selected 4D seismic observables to update dynamic reservoir models. This methodology includes the following steps: (1) create probability maps to select locations where 4D seismic data is assimilated; (2) run a sensitivity analysis; (3) create high-order proxy models; and (4) run an MCMC inversion to determine a set of models that best fit the 4D seismic data and quantify uncertainty. This new workflow has been applied in 3 cases including two synthetic models and one field case. This first synthetic example is called the Imperial College Fault Model (ICFM).The second synthetic model is a fluvial reservoir model with 10 uncertain parameters. The field example is a deepwater turbidite reservoir undergoing a waterflood with a reasonably long production history and high-quality 4D seismic data. Following the four steps of this workflow, all the models are successfully history matched by conditioning to 4D seismic data. Uncertainty quantification was also provided as part of the MCMC inversion. We also compare different scenarios using production data and/or 4D seismic data in the model updating process to show the value of the 4D seismic data. For our field case, the updated models can be used for production forecasting, reserves booking and identification of further development opportunities.
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