Journal of Mining Science

, Volume 45, Issue 2, pp 152–163 | Cite as

Stope design and geological uncertainty: Quantification of risk in conventional designs and a probabilistic alternative

Mineral Mining Technology

Abstract

This paper adopts risk-based concepts developed in open pit mining to the underground stoping environment and shows examples using data from Kidd Creek Mine, Ontario, Canada. Risk is quantified in terms of the uncertainty a conventional stope design has in expected: contained ore tones, grade and economic potential. In addition, a new probabilistic mathematical formulation optimizing the size, location and number of stopes in the presence of grade uncertainty is outlined and applied, to demonstrate the advantages of a user-defined level of acceptable risk.

Keywords

Stope design risk analysis optimization stochastic simulation economic evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.-M. Rendu, “Geostatistical simulations for risk assessment and decision making: the mining industry perspective”, Int. J. Surface Mining, Reclamation and Environment, 16 (2002).Google Scholar
  2. 2.
    M. Vallee, “Resource/reserve inventories: What are the objectives?” CIM Bulletin, 92 (1999).Google Scholar
  3. 3.
    C. K. Baker and S. M. Giacomo, “Resource and reserves: their uses and abuses by the equity markets,” in: Ore Reserves and Finance, A Joint Seminar between Australasian Institute of Mining and Metallurgy and ASX, The Australasian Institute of Mining and Metallurgy, Sydney (1998).Google Scholar
  4. 4.
    P. J. Ravenscroft, “Risk analysis for mine scheduling by conditional simulation,” Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, 101 (1992).Google Scholar
  5. 5.
    P. A. Dowd, “Risk in minerals projects: analysis, perception and management,” Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, 106 (1997).Google Scholar
  6. 6.
    R. Dimitrakopoulos, C. T. Farrelly, and M. Godoy, “Moving forward from traditional optimization: Grade uncertainty and risk effects in open-pit design,” Transactions of the Institution of Mining and Metallurgy Section A: Mining Technology, 111 (2002).Google Scholar
  7. 7.
    R. Dimitrakopoulos, L. Martinez, and S. Ramazan, “A maximum upside / minimum downside approach to the traditional optimization of open pit design,” Journal of Mining Science, 43 (2007).Google Scholar
  8. 8.
    R. Dimitrakopoulos and S. Ramazan, “Uncertainty based production scheduling in open pit mining,” SME Transactions, 316 (2004).Google Scholar
  9. 9.
    S. Ramazan and R. Dimitrakopoulos, “Traditional and new MIP models for production scheduling with insitu grade variability,” Int. J. Surface Mining, Reclamation and Environment, 14 (2004).Google Scholar
  10. 10.
    M. C. Godoy and R. Dimitrakopoulos, “Managing risk and waste mining in long-term production scheduling,” SME Transactions, 316 (2004).Google Scholar
  11. 11.
    J. Ovanic, “Economic optimization of stope geometry,” PhD Thesis, Michigan Technological University, USA (1998).Google Scholar
  12. 12.
    M. Ataee-pour and E.Y. Baafi, “Stope optimization using the maximum value neighborhood (MVN) concept,” in: Twenty-Eighth International Symposium on the Application of Computers and Operations Research in the Mineral Industry, Colorado School of Mines, Golden (1999).Google Scholar
  13. 13.
    G. Thomas and A. Earl, “The application of second-generation stope optimization tools in underground cutoff grade analysis,” in: Strategic Mine Planning, Whittle Programming Pty Ltd., Perth (1999).Google Scholar
  14. 14.
    C. Standing, P. Myers, P. Collier, and M. Noppe, “Orebody modeling and strategic mine planning uncertainty and risk management,” in: Proceedings of Orebody Modeling and Strategic Mine Planning Symposium, The Australasian Institute of Mining and Metallurgy, Melbourne (2004).Google Scholar
  15. 15.
    N. J. Grieco, “Risk analysis of optimal stope design: incorporating grade uncertainty,” PhD Thesis, University of Queensland, Brisbane (2004).Google Scholar
  16. 16.
    N. J. Grieco, “Managing grade risk in stope design optimisation: probabilistic mathematical programming model and application in sublevel stoping,” IMM Transactions, 116 (2007).Google Scholar
  17. 17.
    W. F. Bawden, “Risk assessment in strategic and tactical geomechanical underground mine design,” in: Proceedings of Orebody Modeling and Strategic Mine Planning Symposium, The Australasian Institute of Mining and Metallurgy, Melbourne (2004).Google Scholar
  18. 18.
    P. Roos, Underground Tour Guidebook, Kidd Creek Mine, Ontario (2001).Google Scholar
  19. 19.
    R. Dimitrakopoulos and X. Luo, “Generalized sequential Gaussian simulation on group size v and screen-effect approximations for large field simulations,” Mathematical Geology, 36 (2004).Google Scholar
  20. 20.
    A. Soares, “Direct sequential simulation and co-simulation,” Mathematical Geology, 33 (2001).Google Scholar
  21. 21.
    DATAMINE™, “Floating stope optimizer user guide edition 1.2,” Mineral Industries Computing Limited (1995).Google Scholar
  22. 22.
    P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York (1997).Google Scholar
  23. 23.
    M. H. Kay, “Geostatistical integration of conventional and downhole geophysical data in the metalliferous mine environment,” MSc Thesis, University of Queensland, Brisbane (2001).Google Scholar
  24. 24.
    R. Dimitrakopoulos, “Applied risk assessment in orebody modeling and mine planning: decision-making with uncertainty,” in: Professional Development Short Course Notes, Australasian Institute of Mining and Metallurgy, Melbourne (2007).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2009

Authors and Affiliations

  1. 1.McGill UniversityMontrealCanada

Personalised recommendations