UNDERSTANDING GEOLOGICAL VARIABILITY AND ......UNDERSTANDING GEOLOGICAL VARIABILITY AND QUANTIFYING...

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

§ 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

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

PROJECT LOCATION

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

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

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

‘Main Zone’ (MZ)

‘Bonanza Zone’ (BZ)

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?

BZAu g*m0-250

250-500

500-750

750-1000

KENCANA K1 CASE STUDY – NOV 2006

longsection looking west

November 2006 – K1 Drilled 25m x25m

Stockwork

Cross-Section19900 N

§ Two well defined BZ zones

§ Deterministic wireframe of BZ

Stockwork

NOV 06INTERPRETATION

Isometric view towards west

§ 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

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

KENCANA K1 CASE STUDY – DEC 2008

SUB4SILL

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

§ After nearly 3 years of mill reconciled production:

200,000

400,000

600,000

800,000

1,000,000

1,200,000

K1Cumulative Gold Metal Production - to Dec 2008

Production - Mill Reconciled 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

K1Monthly Gold Metal Production - Dec 2008

-140.0%

-120.0%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

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%

10,000

20,000

30,000

40,000

50,000

60,000

K1Monthly Gold Metal Production - Dec 2008

-140.0%

-120.0%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

-140.0%

-120.0%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

-140.0%

-120.0%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

K1Monthly Gold Metal Variance to Reconciled Production - to Dec 2008

Stockwork

BZMZStockwork

Sub 4 Sill Drive

ACTUAL

Sub 4 Sill Drive

BZ(>60 Au g/t Contour)

Plan View

Stockwork

Sub 4 Sill Drive

ACTUALSub 4 Sill Drive

Plan View

Montly Quarterly Half Yearly Yearly Global Resource

Production Period

K1 Gold Metal Variance % by Production Period - to Dec 2008

Nov 2006 Model - No BZ Constraint Nov 2006 Model - With BZ Constraint

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

CATEGORICAL SIMULATIONSimulation of BZ, MZ ‘lithotype’

+GRADE SIMULATION

Simulation of grade per lithotype

§ Conceptually similar to wireframes except use simulated

lithotypes

§ 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

BZ Probability

Holes with no BZ

Holes with BZ

§ BZ occurs in distinct well defined zones§ BZ probability not constant over field

Plan View

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

BZ Probability

Holes with no BZ

Holes with BZ

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)

Sub 4 Sill Drive

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

1200020

Mined Month

Nov 2006 DDH (25m x 25m)K1 Predicted Monthly Variance

90% Confidence Level

Ore Volume Predicted Gold Production Variance (+-%)

200608 200611 200702 200705 200708 200711 200802 200805 200808 200811

Mining Period

Nov 2006 DDH (25m x 25m)K1 Predicted 3 Monthly Variance

Monthly Quarterly Half Yearly Yearly

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

Simulated BZ (1 Sim)

Simulated MZ (1 Sim)

Wireframe MZ (deterministic)

Mining Schedule

Plan View

Mined Month

Mar 2008 DDH (25m x 25m)K2 Predicted Monthly Variance

Monthly Quarterly Half Yearly Yearly

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

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.

THANK YOU

UNDERSTANDING GEOLOGICAL VARIABILITY AND ......UNDERSTANDING GEOLOGICAL VARIABILITY AND QUANTIFYING...

Documents

Understanding “Clovis” Fluted Point Variability in the ...anthropology.uwo.ca/cje/Publicationpdfs/Debert Fluted Points.pdfArticles Understanding “Clovis” Fluted Point Variability

Training with Heart Rate Variability (HRV) · Training with Heart Rate Variability (HRV) ... Understanding the Basics of Heart-Rate Variability and Avoiding Overtraining 4 . ... simple

Modeling and understanding persistence of climate variability · Modeling and understanding persistence of climate variability ... Six 500 year picntrl simu- ... The seasonal cycle

Data Integrity and Geological Understanding

Geological understanding of plate tectonics: Basic

Understanding subterranean variability: the first genus of

Geological Survey of Canada's contributions to ...inrs.illinois.edu/shilts/pubs/Shilts_1993_Geological Survey of... · Geological Survey of Canada's contributions to understanding

Geological understanding of plate tectonics: Basic …community.dur.ac.uk/yaoling.niu/MyReprints-pdf/2014Niu...Geological understanding of plate tectonics: Basic concepts, illustrations,

Understanding the variability of Australian fire weather ... · and longer-term time scales. At these scales, Australian climate variability is driven by several modes of variability

Understanding existing spatial variability diagnostics at regional scale

Understanding Eastern Africa Rainfall Variability and Change (Towards Improved Prediction of

Modeling and understanding persistence of climate variability · Modeling and understanding persistence of ... the models realistically simulate the contrast ... Modeling and understanding

Understanding Climate Variability and Predictability using Models and Observations: Research at CGAM

Understanding rainfall spatial variability in southeast ...plaza.ufl.edu/gbaigorr/GB/Documents/Journals/Understanding rainfall... · Understanding rainfall spatial variability in

Understanding the genetic basis of phenotype variability

Understanding climate model biases in Southern Hemisphere mid-latitude variability

Understanding Variability in the Cost of a Standard ... · Understanding Variability in the Cost of a Standard Hospital Stay . 4 . ... • Jacqueline Singer, CIHI ... Understanding

Understanding and Accounting for Method Variability in ... · Understanding and Accounting for Method Variability in WET Applications Under the NPDES Program vi June 30, 2000 TABLE

MODULE 1 Understanding climate variability and climate change

Integration of technology & Geological understanding for reservoir … · 2017-11-23 · Integration of technology & Geological understanding for reservoir characterization – A