Mathematical Geosciences

, Volume 45, Issue 8, pp 1005–1020 | Cite as

Forecasting Recoverable Ore Reserves and Their Uncertainty at Morila Gold Deposit, Mali: An Efficient Simulation Approach and Future Grade Control Drilling

  • Richard PeattieEmail author
  • Roussos Dimitrakopoulos
Special Issue


Forecasting of recoverable reserves aims to predict the tonnages and grades that will be recovered at the time of mining. The main concern in this forecasting is the imprecision in the selection of ore/waste resulting from both the so-called information effect or information that becomes available during grade control, and the support effect or mining selectivity during mining. Existing approaches to recoverable reserve estimation account for mining selectivity; however, they largely ignore the information effects from future data becoming available through grade control practices.

An application at the Morila gold deposit, Mali, is utilized in this paper to document a new simulation-based approach for recoverable reserve forecasting that incorporates the potential effects of future grade control data. This accounts for the information effect as well as changes in data quantity and quality over time. In addition, the case study at the Morila mine elucidates the use of a newer, very efficient, and practical alternative to traditional simulation techniques. This direct block simulation method forecasts recoverable reserves directly into the selective mining unit (support) size under consideration. The case study demonstrates the practical uncertainty assessment of the recoverable reserves within the deposit, so that expected inaccuracies in the selection of ore /waste can be accounted for. This allows for fully informed mining decisions to be made that incorporate the effects of information and selectivity while quantifying the potential impact of uncertainty on the mine operation and its final economic outcome.


Recoverable reserves Stochastic simulation Future data 



The financial contribution, data provision, technical support and collaboration from Anglo Gold Ashanti and, in particular, Vaughan Chamberlain are thankfully acknowledged. Some funding from NSERC Discovery Grant 239019-06 was also provided.


  1. Armstrong A, Champigny N (1989) A study on kriging small blocks. CIM Bull 923:128–133 Google Scholar
  2. Benndorf J, Dimitrakopoulos R (2005) New efficient methods for conditional simulation of large orebodies. In: Orebody modelling and strategic mine planning. Spectrum series, vol 14. The Australian Institute of Mining and Metallurgy, Johhanesburg, pp 103–110 Google Scholar
  3. Chiles JP, Delfiner P (2012) Geostatistics, modeling spatial uncertainty, 2nd edn. Wiley, New York CrossRefGoogle Scholar
  4. David M (1973) Tools for planning: variances and conditional simulations. In: Sturgal JR (ed) 11th APCOM symposium, University of Arizona, pp D10–D23 Google Scholar
  5. David M (1977) Geostatistical ore reserve estimation. Elsevier, Amsterdam Google Scholar
  6. David M (1988) Handbook of applied advanced geostatistical ore reserve estimation. Elsevier, Amsterdam Google Scholar
  7. Dimitrakopoulos R, Fonseca M (2003) Assessing risk in grade tonnage curves in a complex copper deposit, northern Brazil, based on an efficient join simulation of multiple correlated variables. In: Application of computers and operations research in the minerals industries. South African Institute of Mining and Metallurgy, Johannesburg, pp 373–382 Google Scholar
  8. Dimitrakopoulos R, Luo X (2004) Generalized sequential Gaussian simulation on group size ν and screen—effect approximations for large field simulations. Math Geol 36:567–591 CrossRefGoogle Scholar
  9. Dimitrakopoulos R, Jewbali A (2013) Joint stochastic optimization of short- and long-term mine production planning: method and application in a large operating gold mine. Trans. Inst. Min. Metall. Sect. A, Min. Technol. 122(2):110–123 Google Scholar
  10. Dowd PA, Dare-Bryan PC (2007) Planning, designing and optimising production using geostatistical simulation. In: Orebody modelling and strategic mine planning, 2nd edn. Spectrum series, vol 14. The Australian Institute of Mining and Metallurgy, Johhanesburg, pp 363–377 Google Scholar
  11. Dowd PA, David M (1976) Planning from estimates: sensitivity of mine production schedules to estimation methods. In: Guarascio M, David M, Huijbregts CD (eds) Advanced geostatistics in the mining industry. Reidel, Dordrecht, pp 163–183 CrossRefGoogle Scholar
  12. Godoy M (2002) The effective management of geological risk in long-term production scheduling of open pit mines. PhD thesis, University of Queensland, Brisbane Google Scholar
  13. Jewbali A (2006) Modelling geological uncertainty for stochastic short-term production scheduling in open pit metal mines. PhD thesis, University of Queensland, Brisbane Google Scholar
  14. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London Google Scholar
  15. Journel AG, Kyriakidis PC (2004) Evaluation of mineral reserves: a simulation approach. Oxford University Press, Oxford Google Scholar
  16. Khosrowshahi S, Shaw WJ, Yeates G (2005) Quantification of risk using simulation of the chain of mining—a case study of Escondida Copper. In: Orebody modelling and strategic mine planning. Spectrum series, vol 14. The Australian Institute of Mining and Metallurgy, Johhanesburg, pp 21–30 Google Scholar
  17. Krige DG (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J Chem Metall Min Soc S Afr 52(6):119–139 Google Scholar
  18. Krige DG (1952) A statistical analysis of some of the borehole values in the Orange Free State goldfield. J Chem Metall Min Soc S Afr Sept:47–64 Google Scholar
  19. Krige DG (1976) A review of development of geostatistics in South Africa. In: Guarascio MM, David M, Huijbregts CJ (eds) Advanced geostatistics in the mining industry: proceedings of the NATO Advanced Study Institute held at the Istituto di Geologia Applicata of the University of Rome, Italy, 13–25 October 1975, pp 279–294 CrossRefGoogle Scholar
  20. Krige DG, Dohm CE (1994) The role of massive grade data bases in geostatistical applications in South African gold mines. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer Academic, Dordrecht, pp 46–54 CrossRefGoogle Scholar
  21. Maréchal A (1984) Recovery estimation: a review of models and methods. In: Verly G et al (eds) Geostatistics for natural resources characterization, part 1. Reidel, Dordrecht, pp 731–744 Google Scholar
  22. Murphy GJ (1982) Some aspects of sampling in terms of mineral exploration and mine geology. In: Symposium on sampling and analysis for the mineral industry. Institution of Mining and Metallurgy, London Google Scholar
  23. Myers DE (1989) To be or not to be… stationarity? That is the question. Math Geol 21(3):347–361 CrossRefGoogle Scholar
  24. Peattie R (2007) The use of simulated future grade control drilling to quantify uncertainty in recoverable reserves. MPhil thesis, University of Queensland, Brisbane Google Scholar
  25. Ramazan S, Dimitrakopoulos R (2012) Production scheduling with uncertain supply: a new solution to the open pit mining problem. Optim Eng. doi: 10.1007/s11081-012-9186-2 Google Scholar
  26. Ravenscroft PJ (1992) Risk analysis for mine scheduling by conditional simulation. Trans Inst Min Metall Ser A, Min Ind 101:A104–A108 Google Scholar
  27. Rossi ME, Parker H (1994) Estimating recoverable reserves: is it hopeless. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer Academic, Dordrecht Google Scholar
  28. Sinclair AJ, Vallée M (1994) Improving sampling control and data gathering for improving mineral inventories and production control. in mineral resource and ore reserve. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer Academic, Dordrecht Google Scholar
  29. Verly G, Sullivan J (1985) MultiGaussian and probability kriging—an application to the Jerrit Canyon deposit. Min Eng June:568–574 Google Scholar

Copyright information

© International Association for Mathematical Geosciences 2013

Authors and Affiliations

  1. 1.AngloGold AshantiJohannesburgSouth Africa
  2. 2.COSMO—Stochastic Mine Planning Laboratory, Department of Mining and Materials EngineeringMcGill UniversityMontrealCanada

Personalised recommendations