The Snap lake diamond deposit - mineable resource.

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2 Title Page: Author: Designation: Affiliation: Address: Tel: The Snap lake diamond deposit - mineable resource. Fanie Nel Senior Mineral Resource Analyst De Beers Consolidated Mines Mineral Resource Management De Beers Consolidated Mines Private Bag X1 South dale Johannesburg (011) (011) (fax) fanie.nel@debeersgroup.com estimating the Keywords: Kimberlite Sequential Indicator Simulation Volume Dilution Post processing

3 The Snap Lake diamond deposit - estimating the mineable resource. Introduction The Snap Lake diamond deposit is located 220 km northeast of Yellowknife, Northwest Territories, Canada. The deposit consists of a number of thin kimberlite sheets, outcropping on a peninsula on the western shore of Snap Lake. It dips to the northeast at 15. The sheet has an aerial extent of approximately 9 km 2. with an average thickness of 3m. Geo/ogl The deposit occurs in the Slave Geological Province, an ancient block of crusta I rocks up to 4Ga old. The country rock geology is predominantly weak to moderately foliated granitoid rocks comprising granodiorites and tonalities. Isolated islands of metamorphosed basaltic volcanic rocks occur within the granitoids. The kimberlite sheet morphology was determined from surface drill holes and underground geological mapping. Of the 293 surface drill holes 127 intersected a single kimberlite stringer or sheet. The remaining holes intersected multiple kimberlite stringers, mostly contained within a 20 m thick package. Figure 1 shows a typical section as mapped underground. IT>] Kimberlite Figure 1 Mapped sidewall section illustrating the variability of the kimberlite sheet. Problem Statement The establishment of a economic resource requires an accurate estimate of the volume of kimberlite that will be mined. The variable morphology dictates that a certain amount of waste wiii be mined, potentially diiuting the resource. The drill holes in the mining area are spaced 150m apart, and cannot be used to deterministically map the sheet morphology. An alternative option has to be sought. A methodology was developed that defines a mining envelope, based on the distribution of kimberlite and waste, subject to mining parameters, that will most probably be extracted during the mining process This option has to: It Utilise both the drill hole data and the drift sidewall mapping. It Give an indication of the expected variability in kimberlite width. It Choose the envelope that will yield the maximum contribution.

4 Simulation as a possible solution The objective is to generate, using indicator simulation, a number of possible kimberlite sheets. Each of these simulations reproduces the statistical characteristics of the real sheet. These include the kimberlite and waste volumes and their spatial relationship, as defined by an indicator variogram. The actual drill hole logs and sidewall mapping are also exactly reproduced. It is impossible to distinguish between the simulated sheet and the real sheet based on the available data and each simulation represents a possible outcome of the true sheet. A 'transfer' function is then invoked that selects the optimal mining envelope and calculates its width, kimberlite width and dilution at each node in each simulation. This produces two dimensional simulations at each grid node. These simulations may be post processed with standard geostatistical techniques to determine the estimated resource and, by comparing the simulations, the confidence inherent in these estimates. Figure 2. n \11 CII Base Da ta Drill hole 10 gs Sidewall m apping I > blc=; 3D Indicator Simulations Mining and Economic I ~ Parameters ' V Transfer Function 20 Simulations:... ~ CII Kimberlite Width CII Envelope Width et Dilution Post Processing " r El El CII "- j! Results E-Type Estimates Confidence Limits Dilution - Tonnage Graphs Figure 2 Estimation flow sheet. Implementation Simulation Sequential Indicator Simulation (SIS) is widely used to simulate geological facies 2. This, cellbased simulation algorithm allows categorical variables, in this case kimberlite and waste, to be

5 stochastically modelled, reproducing both the proportions of each rock type and the correct spatial correlation between them. Aerial and vertical trends in the lithology distribution are accounted for by using a prior, local means model. Agrid, comprising nodes, with a 20m.. 20m m spacing, covering the proposed mining area was used as a base for the simulation. The choice of the grid spacing in the z plane, 0.1 m, was commensurate with the smallest interval logged in the drill holes. The simulation data were constructed using both the drill hole logs and the drift mapping data. The drill holes were composited into 0.1 m indicators, depending on whether an interval contains kimberlite or not. The drift mapping data were digitized from the geologist's logbook into the mine's Gemcom hosted geological model. These sections were then composited to the same resolution as the sampling grid. As both the drill hole and drift data are to the same scale they may be amalgamated in the same database. The data set contained indicators. The kimberlite sheet shows both aerial and vertical trends. It is thickest in the centre of the mining area and thins towards both the North and South. Three domains with distinct vertical kimberlite distribution patterns were identified. In Domain 1 the deposit consists of a single kimberlite stringer. Domain 2 contains multiple kimberlite stringers, of which one is at least three times thicker than the rest. Domain 3 consists of multiple kimberlite stringers without a single dominant one. A prior means model was constructed using a combination of Indicator Simulation and Ordinary Kriging to reproduce these trends. Indicator, Local Means and Residual variograms were fitted and the kimberlite sheet was simulated using the GSLlB 3 SIS software. Figure 3 shows two typical sections through these simulations. --= Figure 3 Sections though typical simulations. (Kimberlite is depicted as black squares.) The sections are a 100m.. 20m. Applying the Transfer Function. A program was written to calculate the optimal mining envelopes using the simulated data. The vertical column of simulated nodes at each aerial xy position can be interpreted as a drill hole through the simulation and a mining envelope can be calculated. The mining envelope had to satisfy the following criteria:., The hangingwall of the envelope must be consistent with the hangingwall of the thickest kimberlite stringer. " The envelope must be chosen so that it will return the maximum revenue, i.e. the combination of waste and ore within the envelope will result in the greatest profit. This requires grade, diamond revenue and mining cost be supplied as input parameters. " The envelope cannot be thinner than a pre-defined minimum mining width, in this case i.5m.

6 The Snap lake diamond deposit - estimating the mineable resource. Introduction The Snap Lake diamond deposit is located 220 km northeast of Yellowknife, Northwest Territories, Canada. The deposit consists of a number of thin kimberlite sheets, outcropping on a peninsula on the western shore of Snap Lake. It dips to the northeast at 15. The sheet has an aerial extent of approximately 9 km 2. with an average thickness of 3m. Geo/ogy1 The deposit occurs in the Slave Geological Province, an ancient block of crustal rocks up to 4Ga old. The country rock geology is predominantly weak to moderately foliated granitoid rocks comprising granodiorites and tonalities. Isolated islands of metamorphosed basaltic volcanic rocks occur within the granitoids. The kimberlite sheet morphology was determined from surface drill holes and underground geological mapping. Of the 293 surface drill holes 127 intersected a Single kimberlite stringer or sheet. The remaining holes intersected multiple kimberlite stringers, mostly contained within a 20 m thick package. Figure 1 shows a typical section as mapped underground. I:)::j Kimberlite Figure 1 Mapped sidewall section illustrating the variability of the kimberlite sheet. Problem Statement The establishment of a economic resource requires an accurate estimate of the volume of kimberlite that will be mined. The variable morphology dictates that a certain amount of waste will be mined, potentially diluting the resource. The drill holes in the mining area are spaced 150m apart, and cannot be used to deterministically map the sheet morphology. An alternative option has to be sought. A methodology was developed that defines a mining envelope, based on the distribution of kimberlite and waste, subject to mining parameters, that will most probably be extracted during the mining process This option has to: Utilise both the drill hole data and the drift sidewall mapping. III Give an indication of the expected variability in kimberlite width. III Choose the envelope that will yield the maximum contribution.

7 Simulation as Cl possible solution The objective is to generate, using indicator simulation, a number of possible kimberlite sheets, Each of these simulations reproduces the statistical characteristics of the real sheet. These include the kimberlite and waste volumes and their spatial relationship, as defined by an indicator variogram. The actual drill hole logs and sidewall mapping are also exactly reproduced. It is impossible to distinguish between the simulated sheet and the real sheet based on the available data and each simulation represents a possible outcome of the true sheet. A 'transfer' function is then invoked that selects the optimal mining envelope and calculates its width, kimberlite width and dilution at each node in each simulation. This produces two dimensional simulations at each grid node. These simulations may be post processed with standard geostatistical techniques to determine the estimated resource and, by comparing the simulations, the confidence inherent in these estimates. Figure 2. Base Data :> Drill hole logs L It Sidewall mapping = 3D Indicator Simulations Mining and Economic I ~ Parameters ~-----,V' Transfer Function 20 Simulations:... ~. Kimberlite Width It Envelope Width Dilution Post Processing r, " Results e E-Type Estimates Confidence Limits Cl Dilution - Tonnage Graphs "- Figure 2 Estimation flow sheet. Implementation Simulation Sequential Indicator Simulation (SIS) is widely used to simulate geological facies 2, This, cellbased simulation algorithm allows categorical variables, in this case kimberlite and waste, to be

8 stochastically modelled, reproducing both the proportions of each rock type and the correct spatial correlation between them. Aerial and vertical trends in the lithology distribution are accounted for by using a prior, local means model. A grid, comprising nodes, with a 20m '" 20m " 0.1 m spacing, covering the proposed mining area was used as a base for the simulation. The choice of the grid spacing in the z plane, 0.1 m, was commensurate with the smallest interval logged in the drill holes. The simulation data were constructed using both the drill hole logs and the drift mapping data. The drill holes were composited into 0.1 m indicators, depending on whether an interval contains kimberlite or not. The drift mapping data were digitized from the geologist's logbook into the mine's Gemcom hosted geological model. These sections were then composited to the same resolution as the sampling grid. As both the drill hole and drift data are to the same scale they may be amalgamated in the same database. The data set contained indicators. The kimberlite sheet shows both aerial and vertical trends. It is thickest in the centre of the mining area and thins towards both the North and South. Three domains with distinct vertical kimberlite distribution patterns were identified. In Domain 1 the deposit consists of a single kimberlite stringer. Domain 2 contains multiple kimberlite stringers, of which one is at least three times thicker than the rest. Domain 3 consists of multiple kimberlite stringers without a single dominant one. A prior means model was constructed using a combination of Indicator Simulation and Ordinary Kriging to reproduce these trends. Indicator, Local Means and Residual variograms were fitted and the kimberlite sheet was simulated using the GSLl8 3 SIS software. Figure 3 shows two typical sections through these simulations. =.-u... ~- Figure 3 Sections though typical simulations. (Kimberlite is depicted as black squares.) The Sections are a 100m * 20m. Applying the Transfer Function. A program was written to calculate the optimal mining envelopes using the simulated data. The vertical column of simulated nodes at each aerial xy position can be interpreted as a drill hole through the simulation and a mining envelope can be calculated. The mining envelope had to satisfy the following criteria:.. The hangingwall of the envelope must be consistent with the hangingwall of the thickest kimberlite stringer. e The envelope must be chosen so that it will return the maximum revenue, i.e. the combination of waste and ore within the envelope will result in the greatest profit. This requires grade, diamond revenue and mining cost be supplied as input parameters... The envelope cannot be thinner than a pre-defined minimum mining width, in this case 1.5m.

9 The thickness, kimberlite content and dilution for each two-dimensional node, in each simulation, is calculated and expressed as GSLlB type grid files for further processing. Post Processing The output from the transfer function program may be processed using conventional geostatistical techniques to gauge some of the properties of the deposit. Two of these techniques will be discussed here. The e-type estimate is the average estimate of a variable for n simulations at any given node. This represents the most likely outcome for that variable. The e-type estimates for the three variables are calculated and averaged into selective mining units. Figure 4 shows the Mining Envelope Width. These estimates may be used in conjunction with conventional mining software to calculate the resource Figure 4 E-Type estimate of the Mining Envelope Width (meters). An6tner us'eful applica'tion is to calculate the confidence limits surrounding th~ estimates, The simulation results are accumulated per selective mining unit. The confidence limits of the estimate for that mining unit are easily deduced from the histogram of the simulation results. A good visual expression of estimation uncertainty is to plot the coefficient of variation (CoV) of all the simulated estimates at any given node. Where the estimates are more uncertain, the CoV of the simulations were be higher than in those areas with better estimates. Figure 5 shows such a plot.

10 Figure 5 Coefficient of Variation - Mining Envelope Width. Conclusion SIS coupled with a transfer function provided a useful way to estimate the Snap Lake resource. The methodology enabled the thickness and kimberlite content of an optimal mining envelope to be calculated conditional to mining and economic parameters. The simulation approach also allows the confidence interval around the estimates and thus uncertainty in these estimates to be gauged. References 1. McGregor, J.A Geological Review of the Snap Lake Property, NWT, of Winspear Resources Ltd. Internal Report. 2. Deutsch, C.V Geostatistical Reservoir Modeling, Oxford University Press Journel, A.G, Deutsch, C.v GSLlB - Geostatistical Software Library and User's Guide, Oxford University Press 1998.

Evaluation of Mineral Resource risk at a high grade underground gold mine

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