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Content Provider | IEEE Xplore Digital Library |
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Author | Changjun Zhu Xiujuan Zhao |
Copyright Year | 2009 |
Abstract | In view of the problem that it is difficult to predict the output in an oilfield which affected by multiple variables, a back propagation (BP) neural network model is built to predict the output in oilfield because the classic statistical method and static model can not meet the demand of precision for the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are used 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 methods. So the BP 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. |
Starting Page | 155 |
Ending Page | 158 |
File Size | 322958 |
Page Count | 4 |
File Format | |
ISBN | 9780769536156 |
DOI | 10.1109/JCAI.2009.93 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-04-25 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | artificial neural network Statistical analysis Biological system modeling Neurons Artificial neural networks Predictive models Educational institutions nonlinear Biological neural networks Hydrology BP neural network prediction Brain modeling output in oilfield Artificial intelligence |
Content Type | Text |
Resource Type | Article |
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