Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks
Publication: International Journal of Geomechanics
Volume 8, Issue 1
Abstract
Diamond bit drilling is one of the most widely used and preferable drilling techniques because of its higher rate of penetration and core recovery in the hardest rocks, the ability to drill in any direction with less deviation, and the ability to drill with greater precision in coring and prospecting drilling. Conventional bit analysis techniques include mathematical methods such as specific energy and formation drillability. In this study, artificial neural network (ANN) analysis as opposed to conventional mathematical techniques is used to estimate major drilling parameters for diamond bit drilling, i.e., weight on bit, rotational speed, and bit type. The use of the proposed methodology is demonstrated using an ANN trained with information obtained from of diamond bit drilling operations conducted on several formations and locations in Turkey. The studied formations include shallow carbonates as well as sandstones in the Zonguldak hard coal basin. The neural network results are compared to those obtained from conventional methods such as specific energy analysis. It was observed that the proposed methodology provided satisfactory results both in relatively less documented and drilled formations as well as in well-known formations.
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References
Akun, E. M. (1997). “Effect of operational parameters and formation properties on drillability in surface set diamond core drilling.” Ph.D. thesis, Middle East Technical Univ., Ankara, Turkey.
Bond, D. F. (1990). “The optimization of PDC bit selection using sonic velocity profiles present in the Timor Sea.” SPEDE, 5, 135–142.
Deere, D. U., and Deere, D. W. (1989). “Rock quality designation (RQD) after twenty years.” Technical Rep. GL-89-1, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss.
Estes, J. C. (1973). “Selecting the proper rotary rock bit.” Drilling, J. F. Schuh, ed., Society of Petroleum Engineers, Dallas, 158–166.
Haykin, S. S. (1994). Neural networks—A comprehensive foundation, Prentice-Hall International, London.
Hecht-Neilsen, R. (1990). Neurocomputing, Addison-Wesley, Reading, Mass.
Hornik, K., Stinchombe, J., and White, H. (1989). “Multilayer feedforward networks are universal approximators.” Neural Networks, 2, 359–366.
Lummus, J. L. (1970). “Drilling optimization.” JPT, 22(11), 1379–1389.
Mason, K. L. (1987). “Three-cone bit selection with sonic logs.” SPEDE, 2, 135–142.
Perrin, V. P., Wilmot, M. G., and Alexander, W. L. (1997). “Drilling index—A new approach to bit performance evaluation.” Proc., SPE/IADC Drilling Conf. Paper SPE 37595, Amsterdam, The Netherlands, 199–205.
Rabia, H. (1985). Oilwell drilling engineering: Principles and practice, Graham & Trotman, Huddersfield, U.K.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning representations by back-propagating errors.” Nature (London), 323, 533–536.
Uboldi, V., Civolani, L., and Zausa, F. (1999). “Rock strength measurements on cuttings as input data for optimizing drill bit selection.” Proc., SPE Annual Technical Conf. and Exhibition, Houston, Paper SPE 56441.
Wilmot, G. M., Calhoun, B., and Perrin, V. P. (1999). “Formation drillability—Definition, quantification, and contributions to bit performance evaluation.” Proc., SPE/IADC Middle East Drilling Technology Conf., Abu Dhabi, UAE, Paper SPE 57558.
Yilmaz, S., Demircioglu, C., and Akin, S. (2002). “Application of artificial neural networks to optimum bit selection.” Comput. Geosci., 28(2), 261–269.
Zhou, W., and Maerz, N. H. (2002). “Identifying the optimum drilling direction for characterization of discontinuous rock.” Assn. Eng. Geolog., Bull., 8(4), 295–307.
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© 2008 ASCE.
History
Received: Jul 31, 2006
Accepted: Aug 1, 2006
Published online: Jan 1, 2008
Published in print: Jan 2008
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