Skip to main content
SpringerLink
Log in

A Novel Rate of Penetration Prediction Model for Large Diameter Drilling: An Approach Based on TBM and RBM Applications

  • MINERAL MINING TECHNOLOGY
  • Published:
Journal of Mining Science Aims and scope

Abstract

In this paper, De Moura and Butt model is extended to the large diameter drilling applications. The model proved to be effective and highly accuracy in predicting drilling performance in 19 distinct RBM and TBM operations even in the presence of datasets with high dispersion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

REFERENCES

  1. Zheng, Y.L. and Zhang, Q.B.Z.J., Challenges and Opportunities of Using Tunnel Boring Machines in Mining, J. Tunnel. Underground Space Technol., 2016, vol. 57, pp. 287–299.

    Article  Google Scholar 

  2. Barla, G. and Pelizza, S., TBM Tunnelling in Difficult Ground Cconditions, Proc. Int. Conf. Geotech. Geol. Eng., Melbourne, 2000.

  3. Graham, P.C., Rock Exploration for Machine Manufacturers, Explor. Rock Eng., 1976, pp. 173–180.

  4. Farmer, I.W. and Glossop, N.H., Mechanics of Disk Cutter Penetration, J. Tunnels Tunnel. Int., 1980, vol. 12, no. 6, pp. 22–25.

    Google Scholar 

  5. De Moura, J., Yang, J., and Butt, S.D., Physics-Based Rate of the Penetration Prediction Model for Fixed Cutter Drill Bits, ASME, J. Energy Res. Technol., 2020, vol. 143, no. 5, pp. 1–12.

    Google Scholar 

  6. Autio, J. and Kirkkomaki, T., Boring of Full Scale Deposition Holes Using a Novel Dry Blind Boring Method, Posiva, Helsinki, 1996.

    Google Scholar 

  7. Hedayatzadeh, M., Shahriar, K., and Hamidi, J.K., An Artificial Neural Network Model to Predict the Performance of Hard Rock TBM, Proc. 6th Asian Rock Mech. Symp., Advances Rock Eng., New Delhi, 2010.

  8. Hassanpour, J., Rostami, J., and Zhao, J., A New Hard Rock TBM Performance Prediction Model for Project Planning, J. Tunnel. Underground Space Technol., 2011, vol. 26, pp. 595–603.

    Article  Google Scholar 

  9. Yagiz, S., Rostami, J., and Ozdemir, L., Colorado School of Mines Approaches for Predicting TBM Performance. Vol. 1, Rock Eng. Technol. Sustainable Underground Construction Eurock, ISRM Int. Symp., Stockholm, 2012,

  10. Ozdemir, L., Development of Theoretical Equations for Predicting Tunnel Borability, Colorado School Mines, Golden, 1977.

    Google Scholar 

  11. Rostami, J. and Ozdemir, L., A New Model for Performance Prediction of Hard Rock TBMs, Proc. Rapid Excav. Tunnel. Conf., Boston, 1993.

  12. Yagiz, S., Development of Rock Fracture and Brittleness Indices to Quantify the Effects of Rock Mass Features and Toughness in the CSM Model Basic Penetration for Hard Rock Tunneling Machines, Colorado School Mines, Golden, 2002.

    Google Scholar 

  13. Yagiz, S., A Model for Prediction of Tunnel Boring Machine Performance, Proc. 10th IAEG Congress, Nottingham, 2006.

  14. Hegde, C., Daigle, H., Millwater, H., and Gray, K., Analysis of Rate of Penetration (ROP) Prediction in Drilling Using Physics-Base and Data-Driven Models, J. Pet. Sci. Eng., 2017, vol. 159, no. 1, pp. 295–306.

    Article  Google Scholar 

  15. Ge, Y., Wang, J., and Li, K., Prediction of Hard Rock TBM Penetration Rate Using Least Square Support Vector Machine, Proc. 13th IFAC Symp. Large Scale Complex Systems: Theory and Applications, Shanghai, 2013.

  16. Ghasemi, E., Yagiz, S., and Ataei, M., Predicting Penetration Rate of Hard Rock Tunnel Boring Machine Using Fuzzy Logic, Bull. Eng. Geol. Env., 2014, vol. 73, pp. 23–35.

    Article  Google Scholar 

  17. Ataei, M., Kakaie, R., Ghavidel, M., and Saeidi, O., Drilling Rate Prediction of an Open Pit Mine Using the Rock Mass Drillability Index, Int. J. Rock Mech. Min. Sci., 2015, vol. 73, pp. 130–138.

    Article  Google Scholar 

  18. Mamaghani, A. S., Avunduk, E., and Bilgin, N., Rock Mechanical Aspects of Excavation Related to Raise Boring Machine—A Typical Example from Asikoy Underground Mine, Kastamonu, Turkey, Proc. ISRM Symp. Eurock 2015, 64th Geomech. Colloquium, Salzburg, 2015.

  19. Rostami, J., Performance Prediction of Hard Rock Tunnel Boring Machines (TBMs) in Difficult Ground, J. Tunnel. Underground Space Technol., 2016, vol. 57, pp. 173–182.

    Article  Google Scholar 

  20. Adoko, A.C., Gokceoglu, C., and Yagiz, S., Bayesian Prediction of TBM Penetration Rate in Rock Mass, J. Eng. Geol., 2017, vol. 226, pp. 245–256.

    Article  Google Scholar 

  21. Salimi, A., Rostami, J.M.C., and Hassanpour, J., Examining Feasibility of Developing a Rock Mass Classification for Hard Rock TBM Application Using Non-Linear Regression, Regression Tree and Generic Programing, J. Geotech. Geol. Eng., 2018, vol. 36, pp. 1145–1159.

    Google Scholar 

  22. Shaterpour-Mamaghani, A., Copur, H., Dogan, E., and Erdogan, T., Development of New Empirical Models for Performance Estimation of a Raise Boring Machine, J. Tunnel. Underground Space Technol., 2018, vol. 82, pp. 428–441.

    Article  Google Scholar 

  23. Armeti, G., Migliazza, M.R., Ferrari, F., Berti, A., and Padovese, P., Geological and Mechanical Rock Mass Conditions for TBM Performance Prediction. The Case of "La Maddalena" Exploratory Tunnel, Chiomonte (Italy), J. Tunnel. Underground Space Technol., 2018, pp. 115–126.

    Article  Google Scholar 

  24. Arbabsiar, M.H., Farsangi, M.A.E., and Mansouri, H., A New Model for Predicting the Advance Rate of a Tunnel Boring Machine (TBM) in Hard Rock Conditions, Min. Geol. Petroleum Eng. Bull., 2020, vol. 35, no. 2, pp. 57–74.

    Google Scholar 

  25. Samaei, M., Ranjbarnia, M., Nourani, V., and Naghadehi, M.Z., Performance Prediction of Tunnel Machine through Developing High Accuracy Equations: A Case Study in Adverse Geological Condition, J. Measurement, 2020, vol. 152, pp. 107–244.

    Google Scholar 

  26. Chen, X., Yang, J., and Gao, D., Drilling Performance Optimization Based on Mechanical Specific Energy Technologies, Drilling, 2018, vol. 1, pp. 133–162.

    Google Scholar 

  27. Khorshidian, H., Butt, S.D., and Arvani, F., Influence of High Velocity Jet on Drilling Performance of PDC Bit under Pressurized Condition, Proc. 48th US Rock Mechanics, Geomech. Symp. ARMA, Minneapolis, 2014.

  28. Wilfing, L., Käsling, H., and Thuro, K., Improvement of Penetration Prediction in TBM-Tunneling by Performing On-Site Penetration Tests, Proc. 13th Int. Congr. Rock Mech., Montreal, 2015.

  29. Shaterpour-Mamaghani, A., Bilgin, N., Balci, C., and Avunduk, E., Predicting Performance of Raise Boring Machines Using Empirical Models, J. Rock Mech. Rock Eng., 2016, vol. 49, pp. 3377–3385.

    Article  Google Scholar 

  30. Lislerud, A. and Vainionpää, P., Application of Raiseboring for Excavating Horizontal Tunnels with Rhino Machines, Posiva, Helsinki, 1997.

    Google Scholar 

  31. Seller, T.W.K., Hard Rock Boring with Tungsten Carbide Insert Big Hole Cutters, Proc. 12th US Symp. Rock Mech. USRMS, Rolla, 1970.

  32. Shaterpour-Mamaghani, A. and Bilgin, N., Some Contributions on the Estimation of Performance and Operational Parameters of Raise Borers—A Case Study in Kure Copper Mine, Turkey, J. Tunnel. Underground Space Technol., 2016, vol. 54, pp. 37–48.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, A1B 3X9, St. John’s, NL, Canada

    J. de Moura, J. Yang & S. D. Butt

Authors
  1. J. de Moura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. D. Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to J. de Moura, J. Yang or S. D. Butt.

Additional information

Translated from Fiziko-Tekhnicheskie Problemy Razrabotki Poleznykh Iskopaemykh, 2023, No. 1, pp. 79-91. https://doi.org/10.15372/FTPRPI20230108.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Moura, J., Yang, J. & Butt, S.D. A Novel Rate of Penetration Prediction Model for Large Diameter Drilling: An Approach Based on TBM and RBM Applications. J Min Sci 59, 70–81 (2023). https://doi.org/10.1134/S1062739123010088

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1062739123010088

Keywords

Search

Navigation