RBF neural network model and its application in the prediction of output in oilfield

Authors:
Changjun Zhu;Yanmin Wang
Affiliations:
College of Urban Construction, Hebei University of Engineering, Handan, China;College of Urban Construction, Hebei University of Engineering, Handan, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks

IEEE Transactions on Neural Networks

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Abstract

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.