Journal of Mining Science

, Volume 47, Issue 2, pp 235–246 | Cite as

A risk quantification framework for strategic mine planning: Method and application

Strategic Mine Planning Under Uncertainty

Abstract

Quantification, assessment and management of orebody uncertainty is critical to strategic mine planning. A method consisting of a series of steps for uncertainty quantification and risk assessment in pit design optimization is outlined here. Multiple simulated scenarios of an orebody’s grade distribution are processed through an established pit optimization approach to produce a distribution of possible outcomes in terms of key project indicators. These indicators are then assessed to support mine planning decisions.

Keywords

Open pit mine design grade risk analysis upside potential downside risk 

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References

  1. 1.
    M. David, Handbook of Applied Advanced Geostatistical Ore Reserve Estimation, Elsevier Science Publishers (1988).Google Scholar
  2. 2.
    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
  3. 3.
    A. G. Journel, “Computer imaging in the minerals industry — Beyond mere aesthetics,” in: APCOM’92 Computer Applications in the Minerals Industries 23rd International Symposium (1992).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.
    R. Dimitrakopoulos, “Conditional simulation algorithms for modelling orebody uncertainty in open pit optimisation,” International Journal of Surface Mining, Reclamation and Environment, 12 (1998).Google Scholar
  6. 6.
    R. Dimitrakopoulos, L. Martinez, and S. Ramazan, “A maximum upside / minimum downside approach to the traditional optimization of open pit mine design,” Journal of Mining Science, 43 (2007).Google Scholar
  7. 7.
    M. Kent, R. Peattie, and V. Chamberlain, “Incorporating grade uncertainty in the decision to expand the main pit at the Navachab gold mine, Namibia, through the use of stochastic simulation,” ” in: The Australasian Institute of Mining and Metallurgy, Spectrum Series, 14 (2007).Google Scholar
  8. 8.
    P. A. Dowd and A. H. Onur, “Open pit optimization — part 1: optimal open-pit design,” Transactions of the Institute of Mining and Metallurgy, Section A: Mining Technology, 102 (1993).Google Scholar
  9. 9.
    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
  10. 10.
    H. Mustapha and R. Dimitrakopoulos, “High-order stochastic simulations for complex non-Gaussian and non-linear geological patterns,” Mathematical Geoscience, 42, No. 5 (2010).Google Scholar
  11. 11.
    J. Wu, T. Zhang, and A. Journel, “Fast FILTERSIM simulation with score-based distance,” Mathematical Geosciences, 40, No. 7 (2010).Google Scholar
  12. 12.
    C. Scheidt and J. Caers, “Spatial Uncertainty Using Distances and Kernels,” Mathematical Geosciences, 41 (2009).Google Scholar
  13. 13.
    H. Lerchs and I. F. Grossmann, “Optimum design of open pit mines,” CIM Bulletin, Canadian Institute of Mining and Metallurgy, 58 (1965).Google Scholar
  14. 14.
    J. Whittle, “A decade of open pit mine planning and optimization — The craft of turning algorithms into packages,” in: APCOM’99 Computer Applications in the Minerals Industries 28th International Symposium (1999).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2011

Authors and Affiliations

  1. 1.Golder Associates SantiagoSantiagoChile
  2. 2.COSMO Lab, Dept Mining and Materials EngineeringMcGill UniversityMontrealCanada

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