UNDERSTANDING GEOLOGICAL VARIABILITY AND QUANTIFYING RESOURCE RISK AT THE
KENCANA UNDERGROUND GOLD MINE, INDONESIA
Ted CouplandPrincipal Geostatistician
Co-Authors:Dale SimsVik SinghDadan WardimanRachel BentonTony Carr
2
AIMS
§ Overview of Kencana operations
§ Resource modeling
§ Resource reconciliation
§ Identify sources of resource variability and uncertainty
§ Performance of resource models for short-term metal prediction
§ Use of Conditional Simulation to quantify variability and risk
3
PROJECT OVERVIEW
§ Kencana project is part of Gosowong gold operations
§ PT NHM (82.5% Newcrest Mining Ltd 17.5% PT Aneka Tambang)
§ Located on Halmahera Island, Indonesia
§ Gold associated with low-sulphidation epithermal veins
§ Gosowong open pit commenced 1999, Toguraci 2002
§ Kencana discovered in 2002, UG mining commenced early 2006
§ Cumulative Gosowong gold production to Dec 2008 >2 Moz
§ Kencana production to Feb 2009 832 kt @ 41.3 g/t Au -> 1.1 Moz
4
PROJECT LOCATION
5
KENCANA GEOLOGY OVERVIEW
§ Low sulphidation epithermal narrow vein system
§ Intersecting network of structures K1, K2, K-Link...
§ K1 1.8 Moz Au
§ K2 1.4 Moz Au
§ low orebody dip 35° – 45°
§ True Width ~7m K1 ~5m K2
§ Strike 500m, Down Dip >300m
>3.2 Moz Au
6
KENCANA GEOLOGY
K1 Main Zone
K2 Main Zone
‘Link’ Zone
Stockwork
K1 Main Zone
K2 Main Zone
‘Link’ Zone
Stockwork
Plan View Cross-Section Looking North
7
KENCANA MINERALISATIONStage 2 – Quartz-adularia
§ Layered quartz, quartz-adularia§ 5 to 50 g/t Au
Stage 3 – Quartz-chlorite
§ Brecciation, quartz-chlorite veining§ Complex geometry, wholly within MZ§ 50 to >200 g/t Au§ 10% Volume 40-50% Metal
1 m
‘Main Zone’ (MZ)
1 m
‘Bonanza Zone’ (BZ)
8
KENCANA K1 CASE STUDY – MAR 2006
§ Initial Estimation – OK into large blocks – 2D Accumulation
§ DDH 25m x 25m to 50m x 50m
§ Mining by underhand cut and fill – poor ground conditions
§ Early Observations (first 6 months mining):§ MZ - continuity and geometry well defined and predictable
§ BZ - extreme short-scale geometric and spatial variability
§ Monthly metal production highly variable and unpredictable
§ COULD RESOURCE MODELS BE IMPROVED?
9
BZAu g*m0-250
250-500
500-750
750-1000
>1000
KENCANA K1 CASE STUDY – NOV 2006
longsection looking west
100m
November 2006 – K1 Drilled 25m x25m
10
BZ
MZ
Stockwork
KENCANA K1 CASE STUDY – NOV 2006
Cross-Section19900 N
§ Two well defined BZ zones
§ Deterministic wireframe of BZ
50m
BZ MZ
Stockwork
NOV 06INTERPRETATION
Isometric view towards west
11
KENCANA K1 CASE STUDY – NOV 2006
§ 171 DDH holes, 25m x25m spacing
§ Two approaches with deterministic constraints:
§ BZ Constrained (BZ wireframe)
§ BZ Un-constrained (MZ wireframe)
§ Estimation:
§ OK into 12.5m x 12.5m blocks
§ 2D Accumulation methodology
§ Re-location into 3D block model
12
KENCANA K1 CASE STUDY - NOV 2006
BZ CONSTRAINED
1.64 Moz Au
BZ UN-CONSTRAINED
1.84 Moz Au
Au g/t
0 - 10
10 - 20
20 - 4040 - 60
>60
100m
longsection looking west
13
KENCANA K1 CASE STUDY – DEC 2008
MZ
SUB4SILL
100m
longsection looking west
UG K1 production to Dec 2008 >1 Moz @ 41.3 g/t Au
14
KENCANA K1 CASE STUDY – DEC 2008
§ After nearly 3 years of mill reconciled production:
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Cum
ulat
ive
Gol
d M
etal
Pro
duct
ion
Au
Oz
Month
K1Cumulative Gold Metal Production - to Dec 2008
Production - Mill Reconciled Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
15
-
10,000
20,000
30,000
40,000
50,000
60,000
Gol
d M
etal
Pro
duct
ion
Au
Oz
Month
K1Monthly Gold Metal Production - Dec 2008
Production - Mill Reconciled Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
KENCANA K1 CASE STUDY – DEC 2008
16
KENCANA K1 CASE STUDY – DEC 2008
-140.0%
-120.0%
-100.0%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
Gol
d M
etal
Var
ianc
e %
to R
econ
cile
d Pr
oduc
tion
Month
K1Monthly Gold Metal Variance to Reconciled Production
Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
-140.0%
-120.0%
-100.0%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
Gol
d M
etal
Var
ianc
e %
to R
econ
cile
d Pr
oduc
tion
Month
K1Monthly Gold Metal Variance to Reconciled Production
Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
-
10,000
20,000
30,000
40,000
50,000
60,000
Gol
d M
etal
Pro
duct
ion
Au
Oz
Month
K1Monthly Gold Metal Production - Dec 2008
Production - Mill Reconciled Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
-140.0%
-120.0%
-100.0%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
Gol
d M
etal
Var
ianc
e %
to R
econ
cile
d Pr
oduc
tion
Month
K1Monthly Gold Metal Variance to Reconciled Production
Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
-140.0%
-120.0%
-100.0%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
Gol
d M
etal
Var
ianc
e %
to R
econ
cile
d Pr
oduc
tion
Month
K1Monthly Gold Metal Variance to Reconciled Production
Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
-140.0%
-120.0%
-100.0%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
Gol
d M
etal
Var
ianc
e %
to R
econ
cile
d Pr
oduc
tion
Month
K1Monthly Gold Metal Variance to Reconciled Production - to Dec 2008
Nov 2006 Resource Model - No BZ Constraint Nov 2006 Resource Model - With BZ Constraint
17
KENCANA K1 CASE STUDY – DEC 2008
Stockwork
BZMZStockwork
Sub 4 Sill Drive
ACTUAL
Sub 4 Sill Drive
NOV 06INTERPRETATION
BZ(>60 Au g/t Contour)
MZ
50m
Plan View
18
KENCANA K1 CASE STUDY – DEC 2008
Stockwork
BZ MZ
Stockwork
Sub 4 Sill Drive
ACTUALSub 4 Sill Drive
NOV 06INTERPRETATION
BZ
MZ
50m
Plan View
19
KENCANA K1 CASE STUDY – DEC 2008
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
Montly Quarterly Half Yearly Yearly Global Resource
Gol
d M
etal
Var
ianc
e %
Production Period
K1 Gold Metal Variance % by Production Period - to Dec 2008
Nov 2006 Model - No BZ Constraint Nov 2006 Model - With BZ Constraint
20
KENCANA K1 – SIMULATION STUDY
§ AIM:
§ Produce realistic geological and grade models capable of
quantifying variability over various reporting periods:
§ monthly, quarterly, six monthly and yearly
§ Using Nov 2006 25m x 25m DDH
§ Repeat for various data configurations
§ Calibrate simulation models to reality
§ Apply methodology to future deposits – K2
21
KENCANA K1 – SIMULATION STUDY
CATEGORICAL SIMULATIONSimulation of BZ, MZ ‘lithotype’
+GRADE SIMULATION
Simulation of grade per lithotype
§ Conceptually similar to wireframes except use simulated
lithotypes
22
KENCANA K1 – SIMULATION STUDY
§ PREPARATION:
§ Accepted the overall MZ wireframe – low risk
§ >60 g/t Au - good indicator of BZ mineralisation (experience)
§ Transform MZ 1m composites into a ‘lithotype’ indicator
§ Produce 2D indicator probability map of BZ
I BZ (x) = 1 Z(x) = 60 Au g/t0 Z(x) < 60 Au g/t
23
KENCANA K1 – SIMULATION STUDY
BZ Probability
Holes with no BZ
Holes with BZ
100m
§ BZ occurs in distinct well defined zones§ BZ probability not constant over field
Plan View
24
KENCANA K1 – SIMULATION STUDY
CATEGORICAL SIMULATION
§ Simulation method - Sequential Indicator Simulation (SIS)
§ ISATIS geostatistical software used for simulations
§ PREPARATION (SIS) :
§ Create 1x1x1m grid – Apply MZ wireframe constraint
§ Variography of BZ indicator – characterise spatial variability
§ 3D OK of BZ indicator into grid – 3D BZ probability map
§ Perform 100 simulations with localised BZ probabilities
§ 100 Equiprobable ‘lithotype’ models
25
KENCANA K1 – SIMULATION STUDY
BZ Probability
Holes with no BZ
Holes with BZ
longsection looking west
26
KENCANA K1 – SIMULATION STUDY
Sub 4 Sill Drive
SIMU 8
SIMU 100
Stockwork
BZMZStockwork
Sub 4 Sill Drive
ACTUAL
Sub 4 Sill Drive
NOV 06INTERPRETATION
BZ(>60 Au g/t Contour)
MZ
50m
Sub 4 Sill Drive
Sub 4 Sill Drive
BZMZ
BZMZ
27
KENCANA K1 – SIMULATION STUDY
GRADE SIMULATION
§ Split composites by ‘lithotype’
§ Independent grade simulations for BZ and MZ
§ Simulation method - Gaussian Turning Bands
§ 1 grade simulation per ‘lithotype’ realization (100 total)
§ 100 Equiprobable ‘geologically controlled’ grade models
§ Gives access to empirical distribution of plausible outcomes
§ Re-group simulations into any ‘support’ or ‘period’
§ Results ranked to derive confidence intervals or variances
28
KENCANA K1 – SIMULATION STUDY
10%
15%
20%
25%
30%
35%
40%
45%
50%
0
2000
4000
6000
8000
10000
1200020
0605
2006
06
2006
07
2006
08
2006
09
2006
10
2006
11
2006
12
2007
01
2007
02
2007
03
2007
04
2007
05
2007
06
2007
07
2007
08
2007
09
2007
10
2007
11
2007
12
2008
01
2008
02
2008
03
2008
04
2008
05
2008
06
2008
07
2008
08
2008
09
2008
10
2008
11
2008
12
Pre
dic
ted
Gol
d P
rod
ucti
on V
aria
nce
(+-%
)
Ore
Vo
lum
e (m
3 )
Mined Month
Nov 2006 DDH (25m x 25m)K1 Predicted Monthly Variance
90% Confidence Level
Ore Volume Predicted Gold Production Variance (+-%)
29
5%
10%
15%
20%
25%
30%
0
5000
10000
15000
20000
25000
200608 200611 200702 200705 200708 200711 200802 200805 200808 200811
Pred
icte
d G
old
Pro
du
ctio
n V
aria
nce
(+-%
)
Ore
Vo
lum
e (m
3 )
Mining Period
Nov 2006 DDH (25m x 25m)K1 Predicted 3 Monthly Variance
90% Confidence Level
Ore Volume Predicted Gold Production Variance (+-%)
KENCANA K1 – SIMULATION STUDY
30
KENCANA K1 – SIMULATION STUDY
0%
5%
10%
15%
20%
25%
30%
Monthly Quarterly Half Yearly Yearly
Pred
icte
d G
old
Prod
ucti
on V
aria
nce
+-%
Production Period
K1 Predicted Gold Metal Variance vs Production Periodand Data Configuration 90% Confidence Level
Sim Nov 2006 DDH 25m x 25m
Sim All DDH Data + 12.5m Sill GC DDH
Sim All DDH + Sills
Sim All DDH + Sills + UC1
Sim All DDH + Sills + UC1 +UC2
Sim All Data (DDH+GCDDH+GC)
Nov06 BZ Un-Constrained Model vs Production
31
KENCANA K2 – SIMULATION STUDY
25 m
Simulated BZ (1 Sim)
Simulated MZ (1 Sim)
Wireframe MZ (deterministic)
Mining Schedule
Plan View
32
KENCANA K2 – SIMULATION STUDY
0%
10%
20%
30%
40%
50%
60%
0
2000
4000
6000
8000
10000
12000
Pre
dic
ted
Go
ld P
rod
ucti
on V
aria
nce
(+-%
)
Ore
Vo
lum
e (m
3 )
Mined Month
Mar 2008 DDH (25m x 25m)K2 Predicted Monthly Variance
90% Confidence Level
Ore Volume Predicted Gold Production Variance (+-%)
33
KENCANA K1 – SIMULATION STUDY
5%
10%
15%
20%
25%
30%
Monthly Quarterly Half Yearly Yearly
Pred
icte
d G
old
Prod
ucti
on V
aria
nce
+-%
Production Period
K1 and K2 Predicted Gold Metal Variance vs Production Period90% Confidence Level
K1 Sim Nov 2006 DDH 25m x 25m
K2 Sim Mar 2008 DDH 25m x 25m
K1 Nov06 BZ Un-Constrained Model vs Production
34
CONCLUSIONS
§ Resource models are robust if considered over a large period.
§ Resource models are inadequate for predicting metal production over short periods Eg. monthly.
§ Deterministic approaches fail to capture true variability.
§ SIS produced realistic models of BZ and MZ geometry.
§ Simulation results were supported by reality.
§ Simulation has improved understanding of geological and metal variability and implications on resource and business risk.
§ Simulation has provided the competent person with an objective basis for resource classification.
35
THANK YOU
K2