PT INCO Ni Laterite Block Modeling_2010!08!23

5/20/2018 PT INCO Ni Laterite Block Modeling_2010!08!23

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PT. International Nickel Indonesia Mines & Exploration

PT. INTERNATIONAL NICKEL INDONESIALATERITE GEOLOGICAL BLOCK MODELING

AND RESOURCE ESTIMATION

Mineral Resources & Mineral Reserves Group PT.INCO, August 2010


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Presentation Outlines

1. Regional & Local Geology

2. Introduction: Block Modeling & Resource Estimation3. Data Validation and Geological Validation4. Geological Domaining and EDA

5. Wireframing, Sample Selection and Compositing6. Variography7. Estimation Parameters

8. Block Model Check9. Volume Variance Correction10.Ore definition11.Resource Classification


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Regional & Local Geology

Jakarta Makassar

peridotites that have been

serpentinised to varying

degrees.

In Sorowako and Petea

the rocks are dominantly

harzburgite and lherzolitewith lesser dunite and rare

pyroxenite.


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Geomorphology

Sorowako

Petea

Matano Lake

Towuti Lake


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West Type Deposit - Profile


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West Type Deposit Bedrock Pinnacle


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East Type Deposit - Profile


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Ore Type ClassificationCLASS PARAMETERS WEST BLOCK ORE TYPES EAST BLOCK & PETEA ORE TYPES

Main ore types -1 Type-1 -1 Type-2 -1 Type-3 -1 Hi Olivine -1 Lo Olivine -6 -18

Ultramafic rock Generally harzburgite with some dunite Generally lherzolite at Sorowako East Block; generally harzburgite at Petea.

Level of serpentinisation Nil 60%

Magnetic susceptibility Very low Generally low Very high

SiO2/MgO ratio 2.2 2.6 1.8 2.0 1.6 1.8 1.4 1.6

Iron content High: 20-24% High: 20-24% Low: 15-18%Optimum screen size -1 -1 -1 -6 -18

Screen rec. ROM to DKP 25-30% 35% 35% 50% 60%

Alternate screen size No alternate size available No alternate size -6 -18 Entire ROM

Screen recovery at alternate size No alternate size available No alternate size 50% 60% 67%

Hardness of boulders Extremely hard Very hard Medium hard Medium hard Soft Soft

Fracture density Very low Medium Medium Medium High Very high Very high

Difficulty of mining High Medium(+) Medium(-) Low Low Low Low

Saprolite thickness (auger) 4.5m Note used for classifying East Block ore types

Olivine content in the benf. ore Medium Medium Medium Low Low Low Low

Olivine content in the rocky frc. High High Low Low Low

OB thickness Medium Generally high except at Petea

Ore thickness High Medium


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Geological Block Modelingand

Resource Estimation


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PT.INCO Strategic Mineral Resources &Mineral Reserves

Exploration

PTI MRMR

Mine Planning / DesignPublic Reporting

(Stock Exchanges)

Project Development Expansion Projects

LOM

PTI Strategic Planning


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Introduction

The following slides summarized the geological block modeling and

resources estimation activity at PT International NickelIndonesia. Improvement and development are conductedcontinuously based on:

1. The yearly MRMR readiness assessment and review2. An external audit findings and recommendations; AMEC (2005,

2007, 2008) and Golder (2010)3. Continuous studies to support the operation and to improve

resources estimation best practices.


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PT.INCO Block Models

Block model productionincreases as the

exploration, drilling

activities, and project

development increase in

all PTI concession area.

PT. INCO Block Models

2921 23

40

69

33

2014 12

24 25

35

3 3 3 36 6

0

10

20

30

40

50

60

70

80

2004 2005 2006 2007 2008 2009

Year

Frequency

block model week manpower

Block Modeling duration increases as

there are improvements in best practice

protocols and raising of major and traceelements

Notes:

2004-2005 Sorowako Project Area (SPA)

2006 SPA and Bahodopi

2007 SPA, Bahodopi, and Pomalaa

2008 SPA, Bahodopi, Pomalaa, SOA, and SCD


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Introduction

Key Factors:

1. The quality of the interpretation (geological domaining) andsubsequent modelling is predicated by a thorough validation ofthe drill hole data with emphasis on chemistry.

2. The quality of the geologic interpretations/wire frames willreflect on the quality of the block model and any conclusionsderived from it.

3. There is no sophisticated geostatistic technique to correct fora poor interpretation. Be rigorous in validating the resultantmodels as mistakes are easy to make but at times hard todetect.


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PT.INCO Resource Estimation General Flow

Exploration Project(geological interpretation, mapping, sampling, drilling, logging)

Sample House

(sample preparation, acquire assays data, sample QAQC)

Geo-Evaluation Validation

(acquire drill hole data, geological domains & validation)

3D Modeling Estimation

(peer review & technical report)

Lab. Process & Technology

Reporting Classification

(Mineral Resources and Mineral Reserves)

Pit Designing Planning

Reconciliation


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PT. INCO Block Modeling General Flow

Grade Estimation (OK)

Assays & Collars

Ore Definition Ni Volume Variance Correction

Data Preparation Create Drillhole

Wireframing

Sample Selection & CompositingBlock Model Prototype

Unfold Composite Samples

Output: Final Block Model with Grade and Product

Parameters: Cut-off Grade,

Ore and Waste Thickness

Prepare Block Model to Vulcan format

Output: Block Model Ready to be Imported to Vulcan

Geo-Evaluation &Data Validation

Geological Domaining &Exploratory data analysis

Yes

No

Variography andEstimation Parameters

Technical Report& Peer Review

Automatic Check

Visual Check

Check using tool

Validation

Fat

alFlaw

Grade Estimation (NN)


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Drill hole Database & Data Validation

Create Assays Data Definition Create Collars Data Definition

Read Assays

Input: Assays Input: Collars

Set Grades value < 0.001 to blanks

Output: Assays.dm Output: Collars.dm

Create Drill holes Database

Output: Drill holes Database (Holes.dm);

Drill Holes summary; Drill Holes Errors

Statistics

Univariate Statistics of Assays

Verificatio

n

VisualCheck

Read Collars

HQ3 ~ 2.406

-1" FRAC -6" FRAC TOT FRAC

REC1 REC6 RECTOT

DW1 DW6 DWTOT

NI1 NI6 NITOT

CO1 CO6 COTOT

FE1 FE6 FETOT

SIO21 SIO26 SIO2TOT

MGO1 MGO6 MGOTOT

CR1 CR6 CRTOT

AL1 AL6 ALTOT

MN1 MN6 MNTOT

CA1 CA6 CATOT

H2O1 H2O6 H2OTOT


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Geo Validation

- geological error.- collars error against topography.

Topography

Fe-MgO


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Geological Domain & EDA

What do we look for?- Does volume properly isolate thedomain being evaluated?

- Is it consistent with the geologicalcontrol rule?

Only related sample should be used

and dont want to mix population


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What do we look for: Outliers, trends, mixed population, bias of any sort & systematicWhy it is important: Garbage in Garbage out


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indication of misclassification


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Geological Model

- topography as top of limonite

- topography as reference for underlying layers


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Wireframing Use GPR (Development)

GPR testing at Songko, January 2010

GPR testing at Songko Quary, January 2010

Drillhole only for Wireframing

Drillhole & GPR for Wireframingslagc

Bloulder

Soft material

Limonite

Saprolite

Bedrock


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Sample Composite

Drill holes Database

Capture samples in layer limonite, saprolite and bedrock

Parameters: composite length,minimum gap, maximum gap,

minimum composite length

Limonite samples Saprolite samples Bedrock samples

Composite Drill holes samples

Composited Limonite Composited Saprolite Composited Bedrock Check compositing

Sample regularization


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Unfolding

Truncated

Proportional

Unfolding illustrations

Transforms a set of

Cartesian CoordinateSystem into Unfolded

Coordinate System [UCS] as

defined by a stratified

geological unit.

Unfold make a spatial

correlation in a proportional

space and geometry.

V i h


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Variography

- 11 elements of experimental variogram in each layer.- Variogram map to evaluate degree of anisotropy.- Zonal anisotropy treatment .

What do we look for?- Quality of the experimental variograms- Sill vs sample variance

- Appropriate model(s) used- Appropriate Ranges

Why is it important?

- Variogram is foundation of geostatistics- Used as a critical input to Kriging- Used to determine weights in estimation- Process is Data driven

E ti ti P t


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Estimation Parameters

Estimation parameter Search strategy

Search ellipsoid

The search strategy should

be honored the drillhole

spacing

Bl k M d l Ch k


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Block Model Check

What do we look for:- Does the model honor the sample data?- Does the model honor the search ranges?- Do all the blocks have been estimated?

Why is it important:- Verify that the estimation parameters were appropriate?- Verify that the correct data was used?

Bl k M d l Ch k


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Block Model Check

Nave QQ Plot

Parallel Modeling: Regardless ofthe method, the global mean shouldstill be the same.

Global Descriptive Statistics

Block Model Check


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Block Model Check

NN Model

OK Model

Visual Inspection Plan View

Plan view visual inspection.Compare the grade distribution

in NN and OK model. Make

sure the estimation run

correctly

Block Model Check


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Block Model Check

Visual Inspection Section ViewCompare the grade distribution in NN and OK model. Make sure the estimation run correctly.

NN Model

OK Model

Volume Variance Correction


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Regression methods such as Kriging may result in an over-smoothing or under-smoothing of

the grade variability producing a block grade distribution with a variance that is lower or higher

than expected. This expected variance can be calculated using Kriges relationship which

states that the dispersion variance for the samples within the deposit is the sum of the

dispersion variance of samples within the blocks and the dispersion variance of the blockswithin the deposit

To capture the variability within short range (distance), the close space drilling is needed. Also

regarding the SMU, the variability within the sort range is needed as the references for the variance

inside the SMU.



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volume variance

calculation

OKNN

COR

Limonite SaproliteOKNN

COR

Block Model Ore Definition


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Block Model Ore Definition

EstimatedBlock Model

Create Simulated Drill holes

Composite Simulated Drill holes in zone LIMONITE and SAPROLITEUsing ORE and WASTE criteria

Parameters:Cut-off Grade (cog),

Minimum Ore Thickness,

Maximum Waste Thickness

Assign ORE and WASTE products to composite using cog

Merge with original simulated Drill holes

Reclassify WASTE above ORE became OB andWASTE below ORE became BZ

Assign BZ product to all bedrock composite

Create Block Model ProductPRODUCT

Block Model

Block Model Transfer


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Block Model Transfer

Datamine Block Model

Block Model converted to Vulcan

The block model ready for external peerreview by:

- Long-term plan mine engineer

- Grade control geologist

If there is no serious findings, the block

model approved by MRMR geologist,

Mine engineers and Grade Control

geologist as valid block model.Peer review MRMR, LTP, GC

Resource Classification


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Resource Classification

Current resource classification criteria:Current resource classification criteria:

+/+/--15% on tonnes and grade on a quarterly basis for measured, on15% on tonnes and grade on a quarterly basis for measured, on

an annual basis for indicated and on a global basis for inferredan annual basis for indicated and on a global basis for inferred (90%(90%level of confidence)level of confidence)

PTI criteria: 25 or 50m for measured, 100m for indicatedPTI criteria: 25 or 50m for measured, 100m for indicated

Measured Indicated Inferred

Drill spacing (m) 25 or 50 100 200

Sampling type Core drill Core drill Core and/or Auger drill

Other improvement in resource classification:Other improvement in resource classification:

Geostatistical drillingGeostatistical drilling Steep slope denser drillingSteep slope denser drilling

PT INCO Mineral Resources & Mineral Reserves


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PT.INCO Mineral Resources & Mineral Reserves

Mineral Reserves

0

100

200

300

400

500

600

DKP(M

t)

2001 2002 2003 2004 2005 2006 2007 2008 2009

MRMR (year)

PTI Mineral Resources

Measured Indicated Inferred

0

10

20

30

40

50

60

70

80

90

100

DK

P

(Mt)

2001 2002 2003 2004 2005 2006 2007 2008 2009

MRMR (year)

PTI Mineral Reserves

Proven Probable

Mineral Resources


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THANK YOUTHANK YOU

Documents

PT INCO Ni Laterite Block Modeling_2010!08!23