Zhou Weidong

Study on Depth Prediction of Abrasive Water Jet Perforation Using Back Propagation Neural Network

Abrasive water jet can be applied to perforate the oil formation rock. The perforation depth generated by abrasive water jet is nonlinearly influenced by so many factors that it is difficult to mathematically correlate the perforation depth with influencing factors. So the back propagation (BP) neural network is introduced to establish the model for predicting perforation depth generated by abrasive water jet. Firstly the fundamentals of BP algorithm are briefly reviewed in this paper. Then regarding the special application of BP network in this research, the methodology of how to select sample set, how to optimize the BP hierarchy and the number of nodes in hidden layer is given in detail. The established BP model is trained and tested by an experimental data set. The test results show that the prediction precision of the BP model can completely meet engineering requirements with the average relative prediction error of only 3.54%.