Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31097
Estimation of the Bit Side Force by Using Artificial Neural Network

Authors: Mohammad Heidari


Horizontal wells are proven to be better producers because they can be extended for a long distance in the pay zone. Engineers have the technical means to forecast the well productivity for a given horizontal length. However, experiences have shown that the actual production rate is often significantly less than that of forecasted. It is a difficult task, if not impossible to identify the real reason why a horizontal well is not producing what was forecasted. Often the source of problem lies in the drilling of horizontal section such as permeability reduction in the pay zone due to mud invasion or snaky well patterns created during drilling. Although drillers aim to drill a constant inclination hole in the pay zone, the more frequent outcome is a sinusoidal wellbore trajectory. The two factors, which play an important role in wellbore tortuosity, are the inclination and side force at bit. A constant inclination horizontal well can only be drilled if the bit face is maintained perpendicular to longitudinal axis of bottom hole assembly (BHA) while keeping the side force nil at the bit. This approach assumes that there exists no formation force at bit. Hence, an appropriate BHA can be designed if bit side force and bit tilt are determined accurately. The Artificial Neural Network (ANN) is superior to existing analytical techniques. In this study, the neural networks have been employed as a general approximation tool for estimation of the bit side forces. A number of samples are analyzed with ANN for parameters of bit side force and the results are compared with exact analysis. Back Propagation Neural network (BPN) is used to approximation of bit side forces. Resultant low relative error value of the test indicates the usability of the BPN in this area.

Keywords: Artificial Neural Network, stabilizer, BHA, horizontal well

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1706


[1] T. B. Adam, K. K. Millheim, M. E. Chenevert, F. S. Young, "Applied Drilling Engineering", vol. 2, 1991, SPE Tex book Series, Dallas, TX, USA.
[2] S. G. Timoshenko, Theory of Elastic Stability, MCGraw-Hill, New York, 1936.
[3] B. Jiazhi, Bottom Hole Assembly Problems Solved by Beam- Column Theory, SPE Int. Meeting of Petroleum Engineering, Beijing SPE10561, 1986.
[4] H. B. Walker, Down hole assembly design increases ROP., World Oil, 1977, pp.59-65.
[5] K. K. Millheim, M. C. Apostal, The effect of bottom hole assembly dynamics on the trajectory of a bit, J. Pet. Technol., 1981, pp. 2323- 2338.
[6] M. Agawani, S. S. Rahman, E. E. Maidla, "BHA Design Algorithm for Extended Reach Wells", SPE Petroleum Computer Conference, Dallas, TX, USA, 1996.
[7] S. Hayken, "Neural Networks: A Comprehensive Foundation", Macmillan College Publishing Co., New York, 1994.
[8] D. E. Rumelhart, G. E. Hinton, R. J. Williams, "Learning Representations by Back Propagating Error", Nature 323, 1986, pp. 533- 536.
[9] R. A. Jacobs, Increased rates of convergence through learning rate adaptation, neural networks, vol. 1, 1988, pp. 295-307.
[10] P. D. Wasserman, Neural Computing: Theory and Practice, Van Nostrand Reinhold, New York, 1989.
[11] J. A. Freeman, Simulating Neural Networks, Addison-Wesley Publishing Company, Inc., New York, 1994.
[12] H. Demuth, M. Beale, M. Hagan, Neural Network Toolbox for Use with MATLAB, The Math Works, Inc, 2006