Computational Geosciences

, Volume 14, Issue 4, pp 691–703 | Cite as

Closed-loop reservoir management on the Brugge test case

  • Chaohui Chen
  • Yudou Wang
  • Gaoming Li
  • Albert C. Reynolds
Original Paper


This paper proposes an augmented Lagrangian method for production optimization in which the cost function to be maximized is defined as an augmented Lagrangian function consisting of the net present value (NPV) and all the equality and inequality constraints except the bound constraints. The bound constraints are dealt with using a trust-region gradient projection method. The paper also presents a way to eliminate the need to convert the inequality constraints to equality constraints with slack variables in the augmented Lagrangian function, which greatly reduces the size of the optimization problem when the number of inequality constraints is large. The proposed method is tested in the context of closed-loop reservoir management benchmark problem based on the Brugge reservoir setup by TNO. In the test, we used the ensemble Kalman filter (EnKF) with covariance localization for data assimilation. Production optimization is done on the updated ensemble mean model from EnKF. The production optimization resulted in a substantial increase in the NPV for the expected reservoir life compared to the base case with reactive control.


Closed-loop reservoir management Optimal control Ensemble Kalman filter Covariance localization Augmented Lagrangian 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Chaohui Chen
    • 1
  • Yudou Wang
    • 2
  • Gaoming Li
    • 1
  • Albert C. Reynolds
    • 1
  1. 1.University of TulsaTulsaUSA
  2. 2.China University of PetroleumDongyingChina

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