Using a support vector machine method to predict the development indices of very high water cut oilfields

Author:

Zhong Yihua,Zhao Lei,Liu Zhibin,Xu Yao,Li Rong

Publisher

Elsevier BV

Subject

Economic Geology,Geochemistry and Petrology,Geology,Geophysics,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference24 articles.

1. Box G E P, Jenkins G M and Reinsel G C. Time Series Analysis Forecasting and Control. Beijing: People Post and Telecommunications Press. 2005. 89–118

2. Chapelle O, Vapnik V N, Bousquet O, et al. Choosing multiple parameters for support vector machines. Machine Learning. 2002a. 46(1–3): 131–159

3. Chapelle O, Vapnik V N and Bengio Y. Model selection for small sample regression. Machine Learning. 2002b. 48(1–3): 9–23

4. Chen M F and Lang Z X. Application of a modified gray model in oilfield production forecast. Xinjiang Petroleum Geology. 2003. 24(3): 246–248 (in Chinese)

5. Cheng J S, Yu D J and Yang Y. Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform. Mechanical Systems and Signal Processing. 2007. 21: 1197–1211

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