Productivity Prediction of Tight Sandstone Reservoir Based on BP Neural Network

Yulei WANG

Abstract


To survey He-8 member tight sand reservoir with low porosity and permeability in Mizhi gas field in Ordos basin, using the conventional well log data, this paper proposes the tight sand reservoir productivity prediction model and classification criterion based on BP neural network, getting quick classification of gas well productivity. We can predict sand reserve quantitatively instead qualitatively with the methods.Applications show that the methods of productivity prediction are effective and practical.

Keywords


Productivity prediction; Low porosity; Low permeability; Tight sandstone; Neural network

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References


Acton, P. D., & Newberg, A. (2006). Artificial neural network classifier for the diagnosis of Parkinson’s disease using [99mTc]TRODAT-1 and SPECT. Physics in Medicine & Biology, 51(12), 3057-3066.

Deng, Y., Huang, R., & Guo, D., et al. (2005). Affecting factors of coal-bed gas production and production prediction of unstable percolation. Natural Gas Industry, 25(1), 117-119.

Hoek, P. J. V. D., Hertogh, G. M. M., & Kooijman, A. P., et al. (1996). A new concept of sand production prediction: Theory and laboratory experiments. Spe Drilling & Completion, 15(4), 261-273.

Kakouei, A., Masihi, M., & Sola, B. S., et al. (2014). Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods. Journal of the Geological Society of India, 84(3), 326-334.

Lee, J. Y., Shin, H. J., & Jong, S. L. (2011). Selection and evaluation of enhanced oil recovery method using artificial neural network. Geosystem Engineering, 14(4), 157-164.

Morita, N., Whitfill, D. L., & Fedde, O. P., et al. (1989). Parametric study of sand-production prediction. SPE Production Engineering, 4(1), 25-33.

Paul, A. P., Fiona, M. D., & Luiz, M. (2001). The interactive effects of strategic marketing planning and performance: A neural network analysis. Journal of Marketing Management, 17(1-2), 159-182.

Serpen, G., Tekkedil, D. K., & Orra, M. (2008). A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis. Computers in Biology & Medicine, 38(2), 204-220.

Shen, Y., & Zhang, A. (2010). The stability classification system of roadway surrounding rock based on VC++ 6.0 and BP neural networks. Proceedings of the International Symposium on Electronic Commerc.

Shi, Z., Shi, Y., & Zhang, H., et al. (2012). Productivity prediction of tight sand reservoir with low permeability in sulige gas field. Well Logging Technology, (06).

Sun, Z. X., Yao, J., & Sun, Z. L., et al. (2011). The application of cluster analysis based on neural network methods in identification reservoir flow unit. Geophysical & Geochemical Exploration, 35(3), 349-353.

Yang, J. S., & An-Qi, L. I. (2008). Dynamic analysis and classification evaluation of CBM well development in Fanzhuang block. Natural Gas Industry, 28(3), 96-98.

Yang, P., Zhu, Q., & Zhong, X. (2009). Subtractive clustering based RBF neural network model for outlier detection. Journal of Computers, 4(8).

Yong, B. Z., & Yong, L., et al. (2011). Classification of carbonate gas condensate reservoirs using well test and production data analyses. Petroleum Science, 8(1), 70-78.

Zeng, B., & Xiang, W. (2007). Application of artificial neural networks on stability evaluation of shifo-temple landslide. Journal of Engineering Geology, 15(Suppl.), 379-385

Zhang, X. F., Tang, J. W., & Wei, Y. S., et al. (2009). Individual well management and dynamic production analysis of sulige gas field. Journal of Southwest Petroleum University, 31(3), 110-114.




DOI: http://dx.doi.org/10.3968/9476

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