Simulation study of oil and water migration modeling based on wavelet neural network

Abstract

An actual physical simulation model is constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistively measuring circuit, then we can obtain large numbers of simulation samples. Considering the issues that the relationship between the remaining oil and every parameters of water displacing oil is a complicated and nonlinear and the advantages of wavelet neural network (WNN), in this paper, the wavelet neural network is used to establish the oil and water migration model. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the network learning algorithm is studied. The simulation results show that this method is feasible and effective.