Hybrid Optimization for Closed-Loop Reservoir Management
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Copyright 2015, Society of Petroleum Engineers. The closed-loop optimization paradigm of an oil field can increase oil recovery and reduce water production, maximizing economic gains. One way to improve the management of a field involves designing optimal production strategies by means of dynamically adjusting the production flow rates or bottom-hole pressures over the reservoir life-cycle and operation. A major difficulty occurs in optimizing production of all wells, according to constraint along the production of a field. These optimal control strategies are often difficult to be realized in practice due to the large number of control variables to be adjusted during the optimization process, requiring large amount of computational infrastructure in place. These challenges become even more evident with larger number of wells and with complex large-scale reservoirs. For these reasons, this work proposes a new hierarchical hybrid optimization framework employing model order reduction techniques in a closed-loop fashion. This paper proposes the use of proper orthogonal decomposition (POD) with the discrete empirical interpolation method (DEIM), to reduce the computational effort, and to perform local optimization by means of gradient-based approach by using forward and adjoint models followed by aggressive line search process. This approach was applied in the UNISIM-I-D benchmark case, testing the performance of the optimization proposed in a complex reservoir with several producer and injector wells, whose conventional optimization would require a high computational cost. The results showed an improvement in reservoir management by means of additional gains in terms of NPV, and through the proposed robust optimization algorithm, we show advantages of the operation of the wells and in the reduction in the computational efforts necessary to attain optimal solutions. The efficiency of the gradient-based approach coupled with model order reduction can be combined in future entire optimization workflow with global optimum algorithms like Fast Genetic Algorithm.
