Automatic reservoir history matching method based on adaptive mutation strategy and differential evolution algorithm

As a classical evolution algorithm， the differential evolution algorithm has the advantages of global search ability， easy implementation， and no gradient. It has been widely used in automatic reservoir history matching. However， the setting of parameters in the algorithm has a significant influence on the result of history matching， and there is convergence stagnation in high-dimensional problems. In order to solve the above problems， an automatic reservoir history matching algorithm was proposed based on the adaptive mutation strategy and differential evolution algorithm. Firstly， based on the principal component analysis method， the high-dimensional parameters of the reservoir model were reduced， and the reduced parameters were used as the parameters adjusted in the differential evolution algorithm to compress the search space of the variables and improve the search efficiency of the algorithm. Secondly， based on the adaptive mutation strategy and differential evolution algorithm， the historical experience in the search process of the algorithm was used to guide the update of the current population. When the individual of the population stopped converging， the mutation strategy of the differential evolution algorithm was switched to change the iterative update mode of the population to avoid the situation that the reservoir parameters stopped optimization and adjustment. In addition， to make the updated model parameters consistent with the prior distribution characteristics， the quantile transformation strategy was applied to transform the distribution of the updated parameters， and the data of non-Gaussian distribution was transformed into Gaussian distribution so that the updated model was more in line with the constraints of the actual geological parameters. The proposed algorithm was tested and verified on a three-dimensional reservoir model. The results show that compared with the traditional differential evolution algorithm framework， the improved differential evolution algorithm can improve the convergence effect of the history matching solution， and the inverted reservoir model parameters are more in line with the actual geological characteristics. Under the same calculation conditions， a better history matching model can be obtained， and the data matching effect is more significant.