Application of the ensemble Kalman filter for assisted layered history matching

Wenshu Zha, Shanlu Gao, Daolun Li, Kaijie Chen

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Abstract


Ensemble Kalman filter (EnKF) method has been used for automatic history matching the well production data such as production rate and watercut. However, the data of the connection watercut and connection rate are rarely used. In this work we conducted a history matching study based on the connection information using the EnKF for the first time to improve the matching accuracy. First, the initial implementation models are generated using the sequential Gaussian simulation method. Second, we choose the well watercut and connection watercut of each layer as production data respectively. During this step, the data such as permeability, pressure, saturation, and production data are normalized to improve the accuracy of history matching and reduce the simulation time. Finally, the case using the well watercut as historical production data is compared against the case using the connection watercut using EnKF. The results show that the well bottomhole pressure and connection watercut can be better matched using the connection watercut as the historical production data. In addition, the simulation time decreases significantly.

Cited as: Zha, W., Gao, S., Li, D., Chen, K. Application of the ensemble Kalman filter for assisted layered history matching. Advances in Geo-Energy Research, 2018, 2(4): 450-456, doi: 10.26804/ager.2018.04.09


Keywords


Layered history matching, ensemble Kalman filter, normalization, production data

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References


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