Abstract: This paper proposes a reservoir inversion method based on radial basis function(RBF) neural network. In this method, the RBF neural network model is constructed by using the bottom hole pressure(BHP) data generated by sampling, and the objective function is defined by the deviation between the predicted value of the RBF neural network and the actual observed value, and then the particle swarm optimization(PSO) algorithm is used to optimize the objective function. Finally, the optimal solution of the uncertain parameters and the inversion parameters are obtained. Compared with the polynomial fitting method, the RBF neural network method has better fitting results and higher precision. Even when the polynomial fitting method fails, the RBF neural network method still works well. A practical example in oilfield shows that this method has good fitting effect, can greatly improve the inversion efficiency, and has a good application prospect.
Keywords: reservoir inversion; radial basis function (RBF) neural network; objective function; optimization algorithm; history matching
合肥工业大学学报自科版