Research on Dynamic Prediction of Oil Saturation with GA-BP Neural Network
Yang Shao-chun · Computer Technology and Development · 2012
In order to overcome the slower convergence rate and falling into local minimal value easily of traditional BP neural network,the genetic algorithm is used for optimization.Then the oil saturation of a certain layer of Z2 fault-block in Jiangsu oilfield is predicted.Firstly,the input and output layer neurons are established.Secondly,an empirical formula is given that realizes the quantification of the input neuron-time.Lastly,the dynamic prediction model on oil saturation of the GA-BP neural network is established after the training with the selected samples.And the oil saturation of 5 years later is predicted with the dynamic prediction model.The prediction result of oil saturation has an important guiding significance to the production practice.