Summary
In recent years, time-lapse (4D) seismic (4DS) data have been widely used for reservoir monitoring to provide relevant information on dynamic changes occurring during production. In complex reservoirs, multiple seismic monitor surveys are usually available. Updating reservoir models with these data can be very beneficial to improve the field’s management. In the quantitative integration of 4DS data into the data assimilation (DA) process, it is crucial to define how to deal with more than one seismic monitor. In this work, we continue a series of investigations about seismic DA procedures and expand on them by analyzing ways to assimilate more than one seismic monitor. More specifically, we evaluate different ways of using production data and two monitor surveys (M3 and M5) to calibrate the dynamic models of a real Brazilian reservoir using the ensemble smoother with multiple data assimilation (ES-MDA) method. We performed the following experiments: (1) sequential assimilation of M3 and M5 with parts of well history divided according to the seismic acquisition dates; (2) assimilation of M3 with the entire well history and subsequent assimilation of M5; (3) assimilation of well and M3 data; and (4) assimilation of well and M5 data. For comparison purposes, we also assimilated only well data. From the results, we observed that well and 4DS data misfits were reduced when assimilating both monitors, compared to the cases where only a single monitor (any of them) was used with production data. This conclusion is also true in the comparison with results obtained when only assimilating well data. This indicates that both seismic monitors are important data to be quantitatively considered in DA. In this particular field, using a previous DA run to solely assimilate the newly available monitor (Case 2) delivered better models and long-term forecasts. Therefore, this would be our recommendation. This study highlights the importance of several 4DS acquisitions for reservoir monitoring and management and shows the challenges of their application in seismic DA for better life cycle field applications.
