Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Abstract

Well production optimization is a complex and time-consuming task in the oilfield development. The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production. This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency. To improve optimization efficiency, a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization (BADS) algorithm is established. This new objective function, which represents the water flooding potential, is extracted from streamline features. It only needs to call the streamline simulator to run one time step, instead of calling the simulator to calculate the target value at the end of development, which greatly reduces the running time of the simulator. Then the well production optimization model is established and solved by the BADS algorithm. The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples. Results demonstrate that the new objective function is positively correlated with the cumulative oil production. And the BADS algorithm is superior to other common algorithms in convergence speed, solution stability and optimization accuracy. Besides, this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods. It can provide a more effective basis for determining the optimal well production for actual oilfield development.