Stope design and geological uncertainty: Quantification of risk in conventional designs and a probabilistic alternative
Mineral Mining Technology
First Online:
Received:
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 evaluationPreview
Unable to display preview. Download preview PDF.
References
- 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.M. Vallee, “Resource/reserve inventories: What are the objectives?” CIM Bulletin, 92 (1999).Google Scholar
- 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.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.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.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.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.R. Dimitrakopoulos and S. Ramazan, “Uncertainty based production scheduling in open pit mining,” SME Transactions, 316 (2004).Google Scholar
- 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.M. C. Godoy and R. Dimitrakopoulos, “Managing risk and waste mining in long-term production scheduling,” SME Transactions, 316 (2004).Google Scholar
- 11.J. Ovanic, “Economic optimization of stope geometry,” PhD Thesis, Michigan Technological University, USA (1998).Google Scholar
- 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.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.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.N. J. Grieco, “Risk analysis of optimal stope design: incorporating grade uncertainty,” PhD Thesis, University of Queensland, Brisbane (2004).Google Scholar
- 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.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.P. Roos, Underground Tour Guidebook, Kidd Creek Mine, Ontario (2001).Google Scholar
- 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.A. Soares, “Direct sequential simulation and co-simulation,” Mathematical Geology, 33 (2001).Google Scholar
- 21.DATAMINE™, “Floating stope optimizer user guide edition 1.2,” Mineral Industries Computing Limited (1995).Google Scholar
- 22.P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York (1997).Google Scholar
- 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.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