An Estimation of Required Rotational Torque to Operate Horizontal Directional Drilling Using Rock Engineering Systems

Document Type: Research Paper

Author

Arak University of Technology, Arak, Iran

Abstract

Horizontal directional drilling (HDD) is widely used in soil and rock engineering. In a variety of conditions, it is necessary to estimate the torque required for performing the reaming operation. Nevertheless, there is not presently a convenient method to accomplish this task. In this paper, to overcome this difficulty based on the basic concepts of rock engineering systems (RES), a model for the estimation of rotational torque to operate horizontal directional drilling is presented. The newly proposed model involves seven parameters (axial force on the cutter/bit (P), rotational speed (revolutions per minute) of the bit (N), the length of drill string in the borehole (L), the total angular change of the borehole (KL), the radius for the ith reaming operation (Di), the mud flow rate (W), and the mud viscosity (V)) effective on required rotational torque to operate horizontal directional drilling while keeping simplicity as well. The performance of the RES model is compared with multiple regression models. The estimation abilities offered using RES and multiple regression models were presented by using field data given from nine projects. The results indicate that the RES-based model predictor with a higher coefficient of determination (R2), a smaller mean square error (MSE), a lower root mean square error (RMSE), and a lower mean absolute percentage error (MAPE) performs better than the other models.
Horizontal directional drilling (HDD) is widely used in soil and rock engineering. In a variety of conditions it is necessary to estimate the torque required for performing the reaming operation. Nevertheless, there is presently not a convenient method to accomplish this task. To overcome this difficult, in this paper, based on the basic concepts of a rock engineering systems (RES), a model for the estimation of rotational torque to operate horizontal directional drilling is presented. The newly proposed model involves 7 effective parameters (axial force on the cutter/bit (P), rotational speed (revolutions per minute) of the bit (N), the length of drill string in the borehole (L), the total angular change of the borehole (KL), the radius for the ith reaming operation (Di), the mud flow rate (W) and the mud viscosity (V)) on required rotational torque to operate horizontal directional drilling with keeping simplicity as well. The performance of the RES model is compared with multiple regression models. The estimation abilities offered using RES and multiple regression models were presented by using field data given from nine projects. The results indicate that the RES based model predictor with higher coefficient of determination (R2) and less mean square error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) performs better than the other models.

Keywords


REFERENCES

1. Gokhale S., Hamm R., and Sterling R. “A Comprehensive Survey on the State of Horizontal Directional Drilling in North America Provides an Inside Look at This Increasingly Growing Industry,” Directional Drilling, 1999, 7(1), 20-23.

2. Lan H., Ma B., Shu B., and Wu Z., “Prediction of Rotational Torque and Design of Teaming Program Using Horizontal Directional Drilling in Rock Strata,” Tunnelling and Underground Space Technology, 2011, 26(2), 415-421.

3. Allouche E. N. “Implementing Quality Control in HDD Projects-a North American Prospective,” Tunnelling and Underground Space Technology, 2001, 16, 3-12.

4. Adel M. and Zayed T. “Productivity Analysis of Horizontal Directional Drilling”, Paper presented at the Pipelines 2009 Conference, Pipelines 2009: Infrastructure’s Hidden Assets, 2009,.

5. Akin S. and Karpuz C. “Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks,” International Journal of Geomechanics, 2008, 8(1), 68-73.

6. Feili Monfared A., Ranjbar M., Nezamabadi-Poor H., Schaffie M., and et al., “Development of a Neural Fuzzy System for Advanced Prediction of Bottomhole Circulating Pressure in Underbalanced Drilling Operations,” Petroleum Science and Technology, 2011, 29(21), 2282-2292.

7. Cancelli A. and Crosta G. “Hazard and Risk Assessment in Rockfall Prone Areas,” Risk and reliability in ground engineering. Thomas Telford, London, 1993, 177-190.

8. Saffari A., Ataei M., and Ghanbari K. “Applying Rock Engineering Systems (RES) Approach to Evaluate and Classify the Coal Spontaneous Combustion Potential in Eastern Alborz Coal Mines,” Int. Journal of Mining & Geo-Engineering, 2013, 47(2), 115-127.

9. Faramarzi F., Farsangi M. E., and Mansouri H. “An RES-based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting,” Rock Mechanics and Rock Engineering, 2013, 46(4), 877-887.

10. Mazzoccola D. and Hudson J. “A Comprehensive Method of Rock Mass Characterization for Indicating Natural Slope Instability,” Quarterly Journal of Engineering Geology and Hydrogeology, 1996, 29(1), 37-56.

11. Moradi M. R. and Farsangi M A. E. “Application of the Risk Matrix Method for Geotechnical Risk Analysis and Prediction of the Advance Rate in Rock TBM Tunneling,” Rock Mechanics and Rock Engineering, 2013, 1-10.

12. Shin H. S., Kwon Y. C., Jung Y. S., Bae G. J., and et al., “Methodology for Quantitative Hazard Assessment for Tunnel Collapses Based on Case Histories in Korea,” International Journal of Rock Mechanics and Mining Sciences, 2009, 46(6), 1072-1087.

13.Benardos A. and Kaliampakos D. “A Methodology for Assessing Geotechnical Hazards for TBM Tunnelling-illustrated by the Athens Metro, Greece,” International Journal of Rock Mechanics and Mining Sciences, 2004, 41(6), 987-999.

14. Faramarzi F., Mansouri H., and Farsangi M. A. E. “Development of Rock Engineering Systems-based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting,” Rock Mechanics and Rock Engineering, 2014, 47(4), 1291-1306.

15. Fattahi H. and Moradi A. “A New Approach for Estimation of the Rock Mass Deformation Modulus: a Rock Engineering Systems-based Model,” Bulletin of engineering geology and the environment, 2017, 1-12.

16. Fattahi H. and Moradi A. “Risk Assessment and Estimation of TBM Penetration Rate Using RES-Based Model,” Geotechnical and Geological Engineering, 2017, 35(1), 365–376.

17. Faramarzi F., Mansouri H., and Ebrahimi Farsangi M. “A Rock Engineering Systems Based Model to Predict Rock Fragmentation by Blasting,” International Journal of Rock Mechanics and Mining Sciences, 2013, 60, 82-94.

18. Bahri Najafi A., Saeedi G. R., and Ebrahimi Farsangi M. A. “Risk Analysis and prediction of Out-of-seam Silution in Longwall Mining,” International Journal of Rock Mechanics and Mining Sciences, 2014, 70, 115-122.

19. Skagius K., Wiborgh M., Ström A., and Morén L., “Performance Assessment of the Geosphere Barrier of a Deep Geological Repository for Spent Fuel: The Use of Interaction Matrices for Identification, Structuring and Ranking of Features, Events and Processes,” Nuclear engineering and design, 1997, 176(1), 155-162.

20. Avila R. and Moberg L. “A Systematic Approach to the Migration of< Sup> 137 Cs in Forest Ecosystems Using Interaction Matrices,” Journal of environmental radioactivity, 1999, 45(3), 271-282.

21. Velasco H., Ayub J., Belli M., and Sansone U., “Interaction Matrices as a First Step Toward a General Model of Radionuclide Cycling: Application to the 137 Cs Behavior in a Grassland Ecosystem,” Journal of radioanalytical and nuclear chemistry, 2006, 268(3), 503-509.

22. Van Dorp F., Egan M., Kessler J. H., Nilsson S., and et al., “Biosphere Modelling for the Assessment of Radioactive Waste Repositories; the Development of a Common Basis by the BIOMOVS II Reference Biospheres Working Group,” Journal of environmental radioactivity, 1998, 42(2), 225-236.

23. Agüero A., Pinedo P., Simón I., Cancio D., and et al., “Application of the Spanish Methodological Approach for Biosphere Assessment to a Generic High-level Waste Disposal Site,” Science of the Total Environment, 2008, 403(1), 34-58.

24. Mavroulidou M., Hughes S. J., and Hellawell E. E. “A Qualitative Tool Combining an Interaction Matrix and a GIS to Map Vulnerability to Traffic Induced Air Pollution,” Journal of Environmental Management, 2004, 70(4), 283-289.

25. Condor J. and Asghari K. “An Alternative Theoretical Methodology for Monitoring the Risks of CO< sub> 2 Leakage from Wellbores,” Energy Procedia, 2009, 1(1), 2599-2605.

26. Fattahi H. “Risk Assessment and Prediction of Safety Factor for Circular Failure Slope Using Rock Engineering Systems,” Environmental earth sciences, 2017, 76(5), 224.

27. Ni┼╝nik D. and Gonet A. “Identification of Rotational Torque and Power in HDD,” Archives of Mining Sciences, 2007, 52(1), 49-60.

28. Maidla E. E. and Wojtanowicz A. K. “A Field Method for Assessing Borehole Friction for Directional Well Casing,” Journal of Petroleum Science and Engineering, 1988, 1(4), 323-333.

29. Jammalamadaka S. R. “Introduction to Linear Regression Analysis,” The American Statistician, 2012, 1-222.

30. Hudson J. A. “Rock Engineering Systems : Theory and Practice,” Horwood, Chichester, 1992.

31. Lu P. and Latham J. P. “A Continuous Quantitative Coding Approach to the Interaction Matrix in Rock Engineering Systems Based on Grey Systems Approaches,” Paper presented at the Proc. 7th Int. Cong. of IAEG., Balkema, Rotterdam, 1994.

32. Fattahi H. “Applying Soft Computing Methods to Predict the Uniaxial Compressive Strength of Rocks from Schmidt Hammer Rebound Values,” Computational Geosciences, 2017, 1-17.

33. Gholami R., Moradzadeh A., Maleki S., Amiri S., and et al., “Applications of Artificial Intelligence Methods in Prediction of Permeability in Hydrocarbon Reservoirs,” Journal of Petroleum Science and Engineering, 2014, 122, 643-656.