JACIII Vol.20 No.7 pp. 1103-1111
doi: 10.20965/jaciii.2016.p1103


Intelligent Nadaboost-ELM Modeling Method for Formation Drillability Using Well Logging Data

Chao Gan*, Weihua Cao*,†, Min Wu*, Xin Chen*, Chengda Lu*, Yule Hu**, and Guojun Wen***

*School of Automation, China University of Geosciences
Wuhan 430074, China

**Faculty of Engineering, China University of Geosciences
Wuhan 430074, China

***School of Mechanical Engineering & Electronic Information, China University of Geosciences
Wuhan 430074, China

Corresponding author

July 5, 2016
September 27, 2016
December 20, 2016
formation drillability, correlation analysis, extreme learning machine, improved Adaboost algorithm, deep area

Drillability is a precondition for drilling-trajectory planning and intelligent control, as well as an important foundation for achieving safety, high quality, and efficiency in deep drilling. A formation drillability modeling method based on the Nadaboost-ELM algorithm is proposed. First, well logging parameters are chosen as inputs of the extreme learning machine (ELM) model, whose output is the formation drillability. Then, the models are trained as weak learners using the improved Adaboost algorithm. Finally, the weak learners are combined into a strong learner. The proposed modeling method is used for regression and prediction. In the regression aspect, the comparison results indicate that the proposed method has higher accuracy than methods in other studies, e.g., multiple regression (MR), grey model (GM), particle-swarm optimization back-propagation (PSO-BP), etc. In the prediction aspect, the results show that the proposed method is better than other prediction methods, e.g., MR, GM, BP, PSO-BP, ELM, and Adaboost-ELM at improving the model’s prediction accuracy, which provides a foundation for intelligent geological drilling.

  1. [1] K. A. Bowker, “Development of the barnett shale play, Fort Worth Basin,” AAPG Bulletin, Vol.91, No.4, 2007.
  2. [2] J. W. Teng, X. M. Ruan, and Y. Q. Zhang, “Geophysical exploration for oil and gas in the second deep space (5000-10000 m) in crust; The only way for the development of fossil energy,” Progress in Geophys, Vol.25, No.2, pp. 359-375, 2010.
  3. [3] M. Ataei, R. Kakaie, M. Ghavidel, et al., “Drilling rate prediction of an open pit mine using the rock mass drillability index,” Int. J. of Rock Mechanics and Mining Sciences, Vol.73, pp. 130-138, 2015.
  4. [4] V. C. Kelessidis, “Rock drillability prediction from in situ determined unconfined compressive strength of rock,” J. of the South African Institute of Mining and Metallurgy, Vol.111, No.6, pp. 429-436, 2011.
  5. [5] O. Su, “Performance evaluation of button bits in coal measure rocks by using multiple regression analyses,” Rock Mechanics and Rock Engineering, Vol.49, No.2, pp. 541-553, 2016.
  6. [6] H. Ma, “Formation drillability prediction based on multi -source information fusion,” J. of Petroleum Science and Engineering, Vol.78, No.2, pp. 438-446, 2011.
  7. [7] H. Ma and Y. Wang, “Formation drillability prediction based on PSOSVM,” IEEE 10th Int. Conf. on Signal Processing Proc., pp. 2497-2500, 2010.
  8. [8] K. Thuro, “Drillability prediction: geological influences in hard rock drill and blast tunnelling,” Int. J. of Earth Sciences, Vol.86, pp. 426-438, 1997.
  9. [9] B. Li, C. L. Yang, and Y, Liu, “Rock mechanical properties according to hardness and plasticity coefficient of rock cuttings,” China J. of Underground Space and Engineering, Vol.1, No.6, pp. 915-917, 2005.
  10. [10] L. He and C. Q. Zhu, “Formation drillability research in the west of Sichuan,” Drilling Production Technology, No.3, pp. 98-99, 2006.
  11. [11] X. J. Liu, J. J. Yan, Y. P. Luo, et al., “Evaluation on rock drillability by well logging data,” Natural Gas Industry, No.7, pp. 69-71, 2005.
  12. [12] C. Ai, H. Y. Wang, and Y. F. Zhang, “Calculating rock drillability for deep gas well drilling in changling fault de-pression,” Natural Gas Industry, Vol.28, No.10, pp. 67-69, 2008.
  13. [13] X. Zhang, Y. H. Zhai, C. J. Xue, et al., “A study of the distribution of formation drillability,” Petroleum Science and Technology, Vol.29, No.2, pp. 149-159, 2011.
  14. [14] R. J. Andrews, G. Hareland, R. Nygaard, et al., “Methods of using logs to quantify drillability,” Rocky Mountain Oil Gas Technology Symposium, Society of Petroleum Engineers, 2007.
  15. [15] X. D. Pan, Z. Liao, and Y. Wang, “Comprehensive mud logging evaluation techniques for marine natural gas reservoirs in Northeast Sichuan,” Mud Logging Engineering, No.2, pp. 55-59, 2014.
  16. [16] B. Rashidi, G. Hareland, and Z. Wu, “Performance, simulation and field application modeling of rollercone bits,” J. of Petroleum Science and Engineering, Vol.133, pp. 507-517, 2015.
  17. [17] S. Yagiz and H. Karahan, “Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass,” Int. J. of Rock Mechanics and Mining Sciences, Vol.80, pp. 308-315, 2015.
  18. [18] H. Y. Zhu, J. G. Deng, Y. H. Xie, et al., “Rock mechanics characteristic of complex formation and faster drilling techniques in Western South China Sea oilfileds,” Ocean Engineering, Vol.44, pp. 33-45, 2012.
  19. [19] H. Basarir, L. Tutluoglu, and C. Karpuz, “Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions,” Engineering Geology, Vol.173, pp. 1-9, 2014.
  20. [20] N. Andreas, K. D. Tamas, E. Asad, et al., “Mathematical modeling applied to drilling engineering: an application of bourgoyne and young ROP model to a presalt case study,” Mathematical problems in engineering, 2015.
  21. [21] R. Arabjamaloei, “A new approach to well trajectory optimization based on rate of penetration and wellbore stability,” Petroleum Science and Technology, Vol.30, No.20, p. 2185, 2012.
  22. [22] S. Akin and C. Karpuz, “Estimating drilling parameters for diamond bit drilling operations using artificial neural networks,” Int. J. of Geomechanics, Vol.8, No.1, pp. 68-73, 2008.
  23. [23] Q. Q. Quan, J. Y. Tang, and S. Y. Jiang, “A real-time recognition based drilling strategy for lunar exploration,” IEEE Conf. on Intelligent Robots and System, pp. 2375-2380, 2014.
  24. [24] M. Nakamura, H. Nomiya, and K. Uehara, “Improvement of boosting algorithm by modifying the weighting rule,” Annals of Mathmatics and Artificial Intelligence, Vol.41, No.1, pp. 95-109, 2004.
  25. [25] H. Q. Xia, Z. D. Liu, P. Chen, et al., “On calculating rock drillbility from log data based on BP neural network technology,” Well Logging Technology, No.2, pp. 148-150, 2004.
  26. [26] Z. D. Liu, H. Q. Xia, P. Chen, et al., “Research on rock drillability predicted by log data on the basis of gray[GM(0,N)] algorithm,” Nature Gas Industry, No.11, pp. 76-78, 2004.
  27. [27] Q. Q. Dong and X. C. Liang, “A model for ppredicting formation drillability based on optimazed BP neural network,” Exploration Engineering: Rock Soil Drilling and Tunneling, No.11, pp. 26-28, 2011.

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Last updated on Jul. 21, 2017