RBF Neural Network Model and its Application in the Prediction of Output in Oilfield
In view of difficulty to predict the output in oilfield which affected by multi-variables, RBF neural network model is set up to predict the output in oilfield because the classic statistics method and static model can not meet the demand of precision to the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are as the input of neural network and oilfield output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy. The results show that the model can forecast the oilfield output with accuracy comparable to other classic method. So the RBF neural network is an effective method to predict the oilfield output with high accuracy. The application of this approach can supply reliable data for the development of oilfield and decrease the risks for the exploitation.
output in oilfield artificial neural network prediction nonlinear RBF neural network
Changjun Zhu Yanmin Wang
College of Urban Construction, Hebei University of Engineering, Handan,056038, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3212-3215
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）