The number of reported applications of the Ensemble Kalman Filter (EnKF) for history matching reservoir models is increasing steadily for various reasons. Here, we report on exploiting the capability of EnKF to handle observations from different sources simultaneously. While traditionally only well data are matched, we use surface subsidence observations together with well data. Surface subsidence results from compaction of the reservoir rock through the mechanical response of the subsurface. Compaction is caused by decreasing pore pressures during reservoir depletion. Therefore, the subsidence data contains information about dynamic pressure distributions in the reservoir.

The joint history matching of well data and surface subsidence observations was applied to the Roswinkel gas field. This field has been operated for 25 years, during which nine leveling campaigns generated a valuable data set of subsidence data. An important feature of the reservoir was the uncertainty about its compartmentalization, due to a large number of possibly sealing faults in the anticlinal structure. Therefore, instead of uncertain rock properties, fault transmissibilities were estimated in this study.

In a previous study on Roswinkel, a compaction field was estimated by inverting subsidence measurements. The results indicated several sealing faults in the reservoir, dividing the field into different compartments with independent pressure histories. The post-inversion history match of production data, however, was unsatisfactory. We have now been able to show that estimating the driving parameters, in casu the fault transmissibilities in the reservoir, can be achieved in a consistent way with both production and surface subsidence data for a synthetic case. Furthermore, for the actual Roswinkel case, the joint history match clearly reveals the discreapancy between the data from both sources and the current reservoir simulation model.

The joint history match of land surface movement data together with well production data is a success for EnKF as a flexible method for history matching. But more importantly, it demonstrates the potential of using complementary sources of information for improved reservoir characterization, and the possibility of estimating the driving parameters.

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