Pre-exploitation mesh optimisation in a Nickel deposits (New- Caledonia) Jacques Deraisme (GV), Olivier Bertoli (GV), Jérôme Michaud (SLN)

Transcription

1 Pre-exploitation mesh optimisation in a Nickel deposits (New- Caledonia) Jacques Deraisme (GV), Olivier Bertoli (GV), Jérôme Michaud (SLN) 1

2 Context Optimising drill spacing pattern: a challenging task. mesh optimization of pre-exploitation drilling remains one of the key factors controlling the confidence in the resource estimation process. Building a platform of conditional simulations & postprocessing quantify the benefits in estimate accuracy when the mesh of drilling is increasing. Simpler geostatistical methods are also available to help with the characterisation of the geometrical uncertainties used in the resource estimation process. 2

3 Objectives The study, performed with Society Le Nickel (SLN) on a New Caledonian deposits, is focused on: 1 st axis, computations of the global estimate error variance to characterise the geometric uncertainties. A two dimensional framework (thickness used as variable). 2 nd axis, work based on the grade accuracy. A characterisation of the accuracy improvement for the grade estimate at different cut-offs and with different pre-exploitation mesh of drilling. A big challenge due to additivity issues (grades are not additives). 3

4 Study Area North Province of New-Caledonia Localization Horizons: (ophiolitic deposits) Laterites (low density) Saprolites (high density) Data*: Exploration (11) Mine Planning (6.5) Pre-exploitation (2.5) *numbers have been changed 4

5 Study Area Data Provided Metric pass (core lines samples) Meshs: Explo (56x28m 2 ), Plan (20x20m Centered), Pre-ex (5x5m 2 ) Geologic codes: Selections from wireframes (proportions deducted) «Core» definition Cf. following slides 5

6 Study Data «Core Zone» Exploration Mesh (56x28m 2 ) Area restricted allows: Realistic time computation for tests Manage an homogenous area (no cluster effect) Nominal Mesh: 20x20m 2 centered (or 14x14m 2 rot 45 ) Plan + Pré-ex 6

7 Study contents Geometric Uncertainties Manage Data/Variography/Results Ni Estimate Accuracy Simulation Framework Modifying pre-ex mesh for sampling Global estimate error computation Statistics analysis of accuracy benefits Secondary variables Manage Data/Variography/Results 7

8 Geometric Uncertainties Wireframes Import of geologic surfaces and proportions/selections computations for: Topography Crust Laterits (economical facies 1) Saprolits (economical facies 2) LAT SAP 8

9 Geometric Uncertainties Studied variables (variography) Thickness Crust (2m) Thickness LAT (6) Thickness SAP (9) Thickness SAP {Ni>tc}: mineralized orebody definition (5m) Theorical 5x5m 2 mesh creation Then, by implemented sampling, 10x10m centered and 10x10m 2 mesh creation & Planification 9

10 Geometric Uncertainties Crust Thickness Core LAT Thickness Core SAP Thickness Core Mineralized SAP 10

11 Geometric Uncertainties Definition of 2D kriging configurations 5x5m Grid and planification block 5x5m Grid and estimation polygon ~1day production ~1month production Basic calculation of equivalent period based on the evaluation of the weekly production 11

12 Geometric Uncertainties Error Variance Tabs planification block of 14m Generic Block Planification 10x10m 10x10m centered 5x5m Crust (relative accuracy) 10% 8% 6% 5% Laterits (relative accuracy) 13% 11% 8% 6% Saprolits (relative accuracy) 18% 15% 12% 10% Sap_Ni>tc (relative accuracy) 42% 37% 33% 26% 12

13 Geometric Uncertainties Error Variance Tabs block 25 pool of 14m planification Generic Block Planification 10x10m 10x10m centered 5x5m Crust (relative accuracy) 5% 4% 3% 3% Laterits (relative accuracy) 7% 5% 4% 4% Saprolits (relative accuracy) 10% 8% 6% 6% Sap_Ni>tc (relative accuracy) 25% 18% 15% 13% 13

14 Geometric Uncertainties Conclusions Significant benefit from 10m to 10m C ; Not significant from 10m C to 5m ; Valuable tightening in the area of high geological complexity. 14

15 Ni Grade Accuracy Need simulations Additivity Issue: estimation needs to model Metal (Ni*Tsm3) & Tonnage (Tsm3) together but selectivity done with Ni (ratio) ; Global Change of support taken into account by a co-simulation set on the SMU(5x5x3 m 3 ) ; As 1 st step, just one cut-off is applied, & the smu selection is unconstrained. 15

16 Ni Grade Accuracy Definition of the area to simulate «erosion» in order to decrease the border effects 16

17 Ni Grade Accuracy Direct Block Simulations Metric pass used for simulating 5x5x1m 3 blocks; (Tsm3) & (Ni*Tsm3) variables are averaged on 5x5x3m 3 blocks; LAT/SAP proportions used for computing the simulated values on the whole blocks; Cut-off applied for calculating the ideal selection for each simulation; From the theorical mesh (5m, 10m C, 10m), estimation of the same values; Results: a statistical distribution of the estimations errors (simulation estimation) 17

18 Ni Grade Accuracy 5x5x1m 3 Direct block Simulations Point Anamorphosis Point Gaussian variables Variographic analysis Model Yx Regularization & Model Y(v) Support Gaussian Correction Yv et v Direct Block Simulation with random extraction of drillholes data within the block 18

19 Ni Grade Accuracy Flowchart simulations per horizon 2. Per sampling mesh (5m, 10m C, 10m) 3. Estimation from theorical grids support 4. Simulation Post-Processing & estimations (e.g. cut-offs selections) 5. Statistical characterisation of the distributions 19

20 Ni Grade Accuracy Results Statistical Analysis Global resources Local estimation Errors 20

21 Ni Grade Accuracy Global Resources «True» 10x10m 10x10m centered 5x5m Tonnage standardized Metal standardized %Ni Conventional Benefit Relative Difference (%) 18% 11.5% 7% 21

22 Ni Grade Accuracy Local Estimate Errors Relative (%) Average stdev erreur Tonnage mesh 5m 8.45 Average stdev erreur Tonnage mesh 10m c Average stdev erreur Tonnage mesh 10m Average stdev erreur Metal mesh 5m Average stdev erreur Metal mesh 10m c Average stdev erreur Metal mesh 10m Average stdev erreur Ni mesh 5m Average stdev erreur Ni mesh 10m c Average stdev erreur Ni mesh 10m

23 Ni Grade Accuracy Conclusions Significant benefit from 10m to 10m c ; Not significant from 10m c to 5m ; Valuable tightening in the high geologic variability area. 23

24 Validation Reproductivity of the Model By the variography By the statistics of the simulations Comparison between the statistics extracted from the simulations (mean & stdev) & the kriging statistics; 24

25 Simulation Variography Reproductivy of the model? From the 100 Q/T Simulations, extraction of the 100 variograms on grids Along the 3 main directions of anisotropy For the main facies (LAT, SAP) Average variograms computation 25

26 Simulation Variography Experimental variograms for each simulation for the LAT facies in one direction (the average variogram is in red) 26

27 Simulation Quality Statistics on the error by simulation groups SIMULATIONS Average stdev erreur Tonnage mesh 5m Average stdev erreur Tonnage mesh 10m c Average stdev erreur Tonnage mesh 10m Average stdev erreur Metal mesh 5m Average stdev erreur Metal mesh 10m c Average stdev erreur Metal mesh 10m

28 Simulations Average Histograms: Kriged simulation Averaged in on postprocessing 50 Simulations Average Histograms Tonnage/Metal for LAT 28

29 Conclusions 29

30 Conclusions 1. Highlight the benefit of the 10m c pre-ex mesh; 2. In geologically complex area, work with a tight mesh, 5x5m 2 ; direct 5x5x3m 3 block (co-)simulations (at the deposit scale) of the variables Tonnage & Metal variables in both facies (LAT & SAP) ; 4. Take into account the Information effect: do a kriging with a 10m c mesh, extracted from each simulated grid; 5. Statistical post-processing: average recoverable Q & T for the 20x20x3m 3 panels. 30