Search

Limited by the computing capacity of current computer and difficulty in inversion of large quantity of reservoir parameters for large scale reservoir history matching, the current automatic history matching methods are still hard to be applied in real fields. A parameterization method based on matrix decomposition is proposed to reduce the computational cost and improve the efficiency of history matching without decreasing the precision by mapping the original optimization problem from high dimensional space to low dimensional space. Base on the updated reservoir model after history matching, a reservoir production optimization control model is established. Considering the complexity of gradient calculation for history matching and production optimization objective function, a new general upscaling stochastic approximation (GUSA) algorithm is given based on the approximation perturbation gradient algorithm. The GUSA algorithm aims to get maximum increment of an objective function in unit step by obtaining an optimal lower triangular matrix in each iterative step. Toy problem results show that the calculation efficiency is improved much compared with the simultaneous perturbation stochastic approximation (SPSA) algorithm. For application of a real reservoir, a good history matching result is obtained for historic production data that verifies the validity of parameterization method and GUSA algorithm. An optimal control for production optimization performs an increase of oil production and a decrease of water cut. Besides, the optimal production scheme is accordant with the practical characteristics of dynamic production, which also proves the availability of GUSA algorithm.