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

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.

[1]  D. Krige A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author , 1951 .

[2]  Massimo Guarascio,et al.  Advanced Geostatistics in the Mining Industry , 1977 .

[3]  Peter A. Dowd,et al.  Planning from Estimates: Sensitivity of Mine Production Schedules to Estimation Methods , 1976 .

[4]  Arja Jogita Jewbali MODELLING GEOLOGICAL UNCERTAINTY FOR STOCHASTIC SHORT-TERM PRODUCTION SCHEDULING IN OPEN PIT METAL MINES , 2006 .

[5]  R. Dimitrakopoulos,et al.  Assessing Risk in Grade-Tonnage Curves in a Complex Copper Deposit, Northern Brazil, Based on an Efficient Joint Simulation of Multiple Correlated Variables , 2003 .

[6]  Eric R. Ziegel,et al.  Geostatistics for the Next Century , 1994 .

[7]  R Dimitrakopoulos,et al.  Joint stochastic optimisation of short and long term mine production planning: method and application in a large operating gold mine , 2013 .

[8]  R. Bateman Orebody Modelling and Strategic Mine Planning.R. Dimitrakopoulos, Editor. Pp 402. The Australasian Institute of Mining and Metallurgy Spectrum Series 14. Second edition. 2007. ISBN 978-1-920806-76-7. Price (outside Australia) hardcopy A$80.00, CD-ROM A$60.00. , 2008 .

[9]  Alain Marechal,et al.  Recovery Estimation: A Review of Models and Methods , 1984 .

[10]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[11]  Roussos Dimitrakopoulos,et al.  Generalized Sequential Gaussian Simulation on Group Size ν and Screen-Effect Approximations for Large Field Simulations , 2004 .

[12]  Salih Ramazan,et al.  Production scheduling with uncertain supply: a new solution to the open pit mining problem , 2013 .

[13]  David Michel,et al.  Geostatistical Ore Reserve Estimation , 1977 .

[14]  Marcelo Godoy,et al.  The effective management of geological risk in long-term production scheduling of open pit mines , 2003 .

[15]  M. David Handbook of Applied Advanced Geostatistical Ore Reserve Estimation , 1987 .

[16]  Andre G. Journel,et al.  Geostatistics for Natural Resources Characterization: Part 1 , 2013 .

[17]  M. E. Rossi,et al.  Estimating Recoverable Reserves: Is it Hopeless ? , 1994 .

[18]  M. Vallée,et al.  Improved Sampling Control and Data Gathering for Improved Mineral Inventories and Production Control , 1994 .

[19]  Richard John Reid Peattie THE USE OF SIMULATED FUTURE GRADE CONTROL DRILLING TO QUANTIFY UNCERTAINTY IN RECOVERABLE ORE RESERVES , 2007 .

[21]  Andre G. Journel Evaluation of mineral reserves , 2004 .

[22]  Massimo Guarascio,et al.  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 , 1976 .

[23]  D. G. Krige A statistical analysis of some of the borehole values in the Orange Free State Goldfield , 1952 .

[24]  D. Krige,et al.  The Role of Massive Grade Data Bases in Geostatistical Applications in South African Gold Mines , 1994 .

[25]  Donald E. Myers,et al.  To be or not to be... stationary? That is the question , 1989 .