Research on well production prediction based on improved extreme learning machine Online publication date: Tue, 16-Jun-2015
by Wenbo Na; Quanmin Zhu; Zhiwei Su; Qingfeng Jiang
International Journal of Modelling, Identification and Control (IJMIC), Vol. 23, No. 3, 2015
Abstract: To overcome traditional extreme learning machine (ELM) disadvantages in over-fitting problem and the local minimum, consequently to improve the prediction accuracy in production of oil well, this paper proposes an improved extreme learning machine (RWELM) prediction model so that the structural risk minimisation principle is integrated into the model and the common activation function is substituted by wavelet function. So we get the improved extreme learning machine algorithm (RWELM) and give the parameter selection method. This paper takes dynamic data from an oil well production of LunNan (China) oilfield as the research background to demonstrate the new approach in forecasting production of oil well. The experimental results show that the forecasting model is better than ELM, BP (back propagation) network with time delay sequence, LM-BP neural networks in both generalisation performance and predictive accuracy.
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