Application of Support Vector Machine in Friction Coefficient Prediction for Extended-Reach Well

Yun LI

Abstract


Torque and drag is a major problem in the drilling process for extended-reach well due to the great well depth and large displacement. The value of friction & torque is mainly determined by friction coefficient value, and there are many factors affecting the coefficient friction, reasonable and correct determination of the friction coefficient is an issue that must be addressed in the friction & torque analysis and prediction. On the basis of the friction coefficient calculation, the prediction model of friction coefficient for designing well was established based on support vector machine, the results show its prediction accuracy is over 90%, the limitation of using experiences to determine friction coefficient was broken down in the process of well designing.


Keywords


Torque and drag; Extended-reach well; Support vector machine; Friction coefficient; Prediction

Full Text:

PDF

References


[1] Johancsik, C. A., Friesen, D. B., & Dawson, R. (1984). Torque and drag in directional wells–prediction and measurement. Journal of Petroleum Technology, 36(6), 987-992.

[2] Zhang, L. Q. (2008). Calculating model of torque and drag in extended reach well. Fault-Block Oil & Gas Field, 15(2), 88-90.

[3] Fan, X. W., & Du, S. X. (2004). Rough support vector machine and its application to waste water treatment process. Control and Decision, 19(5), 573-576.

[4] Wei, X. G. (2003). Research on handwritten numerical digits recognition based on kernel-based learning algorithm (Doctoral dissertation). Nanjing University of Science and Technology, Nanjing.

[5] Burbidge, R., Trotter, M., & Buxton, B. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computer and Chemistry, 26(1), 5-14.

[6] Trotter, M. W. B., Buxton, B. F., & Holden, S. B. (2001). Support vector machines in combinatorial chemistry. Measurement and Control, 34(8), 235-239.

[7] Van Gestel, T., Suykens, J. A. K., & Baestaens, D. E. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12(4), 809-821.

[8] Burges, C. A. (1998). Tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 1-47.

[9] Suykens, J. A. K., Vandewalle, J., & De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14(1), 23-35.

[10] Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines (pp.169-182). Cambridge: The MIT Press.




DOI: http://dx.doi.org/10.3968/7501

Refbacks

  • There are currently no refbacks.


Copyright (c) 2015 Advances in Petroleum Exploration and Development



Share us to:   

Reminder

 We are currently accepting submissions via email only.

The registration and online submission functions have been disabled.

Please send your manuscripts to aped@cscanada.net，or  aped@cscanada.org  for consideration. We look forward to receiving your work.

 

We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; aped@cscanada.net; aped@cscanada.org

 Articles published in Advances in Petroleum Exploration and Development are licensed under Creative Commons Attribution 4.0 (CC-BY). 

ADVANCES IN PETROLEUM EXPLORATION AND DEVELOPMENT Editorial Office

Address:1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.

Telephone: 1-514-558 6138
Website: Http://www.cscanada.net
Http://www.cscanada.org
E-mail:office@cscanada.net;  office@cscanada.org

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures