Abstract
In the course of oilfield development, accurate measurement of water content in crude oil has always been playing important role in practicing development adjustment and enhancing the effects of stimulation operation, and moreover it determines the development perspectives of oilfield. After research of the method of measuring the rate of water content, and the non-linear mapping relation between the rate of water content and impact factors of crude oil, a model of predicting the rate of water content in crude oil about vertical well based on wavelet neural network is proposed. Results of the simulation in MATLAB indicated that the way of WNN (wavelet neural network) has better convergent rate, prediction precision, learning ability and generalization ability than the traditional BP neural network. The method of WNN can predict the water content in crude oil with high precision and it owns much more powerful theoretical guide and much better application effects. It will have a broad application in the future.
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References
Ming-yi, C., Xin-rong, Y.: Information Merge Technology of Multiple Transducers and Its Application in Moisture Measurement of Oil Products. J. Process Automation Instrumentation 23(8), 2–3 (2002)
Shun-tian, L., Yang, S.: The Systems Analysis and Design Based on Matlab-Neural Network. Xidian University Press, Xi’an (1999)
Tsaih, R., Hsu, Y.-s., Lai, C.C.: Forecasting S&P 500 Stock Index: Futures with a Hybrid AI System. J. Decision Support System, 161–174 (1998)
Jin-qi, W., Xi-fu, Q., Kui-yong, Z.: Coaxial Transmission Line Phase Method for Measuring Water Content of Oil Well. J. Chinese Journal of Scientific Instrument 23(1), 75–76 (2002)
Jin-qi, W., Xi-fu, Q., Jian-ming, C.: Fhase measurement of water content in oil well. J. Journal of Harbin Institute of Technology 34(2), 245–247 (2002)
Jin-qi, W., Xi-fu, Q., Ying-hua, Y.: Test Study of Water Cut Tool in Oil Well Based on Phase Method. Acta Metrologica Sinica 25(4), 366–368 (2004)
Xin, W., Lu, Z., Xiang, L.: Simulation and Application of MATLAB Neural Network. Science Press (2003)
Li-qun, H.: Artifical Neural Network Course Book. Beijng University of Posts and Telecommunications Press, Beijing (2006)
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© 2010 Springer-Verlag Berlin Heidelberg
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Niu, H., Liu, C., Wang, J., Sun, X. (2010). Application of Wavelet Neural Network to Prediction of Water Content in Crude Oil. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_3
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DOI: https://doi.org/10.1007/978-3-642-12990-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
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