Application of wavelet neural network to prediction of water content in crude oil

Abstract

In the course of oilfield development, accurate measurement of water content in crude oil has always been playing important role in practicing development adjustment and enhancing the effects of stimulation operation, and moreover it determines the development perspectives of oilfield. After research of the method of measuring the rate of water content, and the non-linear mapping relation between the rate of water content and impact factors of crude oil, a model of predicting the rate of water content in crude oil about vertical well based on wavelet neural network is proposed. Results of the simulation in MATLAB indicated that the way of WNN (wavelet neural network) has better convergent rate, prediction precision, learning ability and generalization ability than the traditional BP neural network. The method of WNN can predict the water content in crude oil with high precision and it owns much more powerful theoretical guide and much better application effects. It will have a broad application in the future. © 2010 Springer-Verlag Berlin Heidelberg.