Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Armstrong A, Champigny N (1989) A study on kriging small blocks. CIM Bull 923:128–133
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
Chiles JP, Delfiner P (2012) Geostatistics, modeling spatial uncertainty, 2nd edn. Wiley, New York
David M (1973) Tools for planning: variances and conditional simulations. In: Sturgal JR (ed) 11th APCOM symposium, University of Arizona, pp D10–D23
David M (1977) Geostatistical ore reserve estimation. Elsevier, Amsterdam
David M (1988) Handbook of applied advanced geostatistical ore reserve estimation. Elsevier, Amsterdam
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
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
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
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
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
Godoy M (2002) The effective management of geological risk in long-term production scheduling of open pit mines. PhD thesis, University of Queensland, Brisbane
Jewbali A (2006) Modelling geological uncertainty for stochastic short-term production scheduling in open pit metal mines. PhD thesis, University of Queensland, Brisbane
Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London
Journel AG, Kyriakidis PC (2004) Evaluation of mineral reserves: a simulation approach. Oxford University Press, Oxford
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
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
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
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
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
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
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
Myers DE (1989) To be or not to be… stationarity? That is the question. Math Geol 21(3):347–361
Peattie R (2007) The use of simulated future grade control drilling to quantify uncertainty in recoverable reserves. MPhil thesis, University of Queensland, Brisbane
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
Ravenscroft PJ (1992) Risk analysis for mine scheduling by conditional simulation. Trans Inst Min Metall Ser A, Min Ind 101:A104–A108
Rossi ME, Parker H (1994) Estimating recoverable reserves: is it hopeless. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer Academic, Dordrecht
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
Verly G, Sullivan J (1985) MultiGaussian and probability kriging—an application to the Jerrit Canyon deposit. Min Eng June:568–574
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Peattie, R., Dimitrakopoulos, R. Forecasting Recoverable Ore Reserves and Their Uncertainty at Morila Gold Deposit, Mali: An Efficient Simulation Approach and Future Grade Control Drilling. Math Geosci 45, 1005–1020 (2013). https://doi.org/10.1007/s11004-013-9478-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11004-013-9478-x