UNDERSTANDING GEOLOGICAL VARIABILITY AND ......UNDERSTANDING GEOLOGICAL VARIABILITY AND QUANTIFYING RESOURCE RISK AT THE KENCANA UNDERGROUND GOLD MINE, INDONESIA Ted Coupland Principal

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


Cross-Section19900 N

§ Two well defined BZ zones

§ Deterministic wireframe of BZ

50m

BZ MZ

Stockwork

NOV 06INTERPRETATION

Isometric view towards west

11


§ 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


13

KENCANA K1 CASE STUDY – DEC 2008

MZ

SUB4SILL

100m


UG K1 production to Dec 2008 >1 Moz @ 41.3 g/t Au

14


§ 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



16


-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



-

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


-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



-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



-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


17


Stockwork

BZMZStockwork

Sub 4 Sill Drive

ACTUAL

Sub 4 Sill Drive


BZ(>60 Au g/t Contour)

MZ

50m

Plan View

18


Stockwork

BZ MZ

Stockwork

Sub 4 Sill Drive

ACTUALSub 4 Sill Drive


BZ

MZ

50m

Plan View

19


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


CATEGORICAL SIMULATIONSimulation of BZ, MZ ‘lithotype’

+GRADE SIMULATION

Simulation of grade per lithotype

§ Conceptually similar to wireframes except use simulated

lithotypes

22


§ 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


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


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


BZ Probability

Holes with no BZ

Holes with BZ


26


Sub 4 Sill Drive

SIMU 8

SIMU 100

Stockwork

BZMZStockwork

Sub 4 Sill Drive

ACTUAL

Sub 4 Sill Drive


BZ(>60 Au g/t Contour)

MZ

50m

Sub 4 Sill Drive

Sub 4 Sill Drive

BZMZ

BZMZ

27


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


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




30


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


25 m

Simulated BZ (1 Sim)

Simulated MZ (1 Sim)

Wireframe MZ (deterministic)

Mining Schedule

Plan View

32


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



33


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

Documents

UNDERSTANDING GEOLOGICAL VARIABILITY AND ......UNDERSTANDING GEOLOGICAL VARIABILITY AND QUANTIFYING RESOURCE RISK AT THE KENCANA UNDERGROUND GOLD MINE, INDONESIA Ted Coupland Principal