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Geology and Conversion to

Practical Parameters(Boreholes to Reserves)

Ian de Klerk

SACPS Conference Vryheid

24 May 2006

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COAL COAL

RESOURCES RESERVESReported as in situ Reported as

estimates Mineable In Situ,

ROMand Saleable

estimates

INFERRED

Increasing

level of INDICATED PROBABLE

geoscientific Mineable In-situ,

knowledge ROM,

and Saleable

confidence

MEASURED PROVED

Mineable In-situ,

ROM,

Saleable

Consideration of mining, coal processing, economic, marketing, legal

environmental, social and governmental factors

(the 'modifying factors')

SANS 10320:2004 - The Guideline

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GEOLOGICAL MODEL

BUILDING

GEOLOGICAL DATA

EVALUATION

REPORTED COAL

RESOURCES

Coal Resources per

Classification Category

GEOLOGICAL MODEL

OPTIMISATIONAnalysis of data integrity,

distribution, spatial data density

and confidence classification for

both Structural and Coal Quality

Data Sets

Confidence Classification into

Measured, Indicated and Inferred

Categories for both structural

and coal quality models

CLASSIFICATION

Sub-seam definition,

correlation and structural

interpretation

ROM DISCOUNT FACTORS

SALES DISCOUNT FACTORS

MINING AND SALES

DISCOUNT FACTORS

REPORTED COAL

RESERVES

Coal Reserves per


Classification into

Proven and Probable Reserve

Categories

CLASSIFICATION

LIFE OF MINE PLANMine Planning, Layouts,

Schedule = Economic Life of

Mine Model

MTIS

Application of Geological cut-

offs and losses

RESOURCE AND RESERVE ESTIMATION

Geological Domains,

Mining Horizon selection,

Optimisation of Interpolators

Confirmation of the estimated

discount factors from GTIS to

ROM and SALES within the

area mined

RECONCILIATION

TECHNICAL

MODIFYING FACTORSENVIROMENTAL, LEGAL

GOVERNMENTAL, ECONOMIC

INPUTS

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The Geological Model - Resources

Topographic DTM

Borehole Collar Survey

Seam / Sub-Seam Intervals and

Correlation

Sample Intervals, SampleRepresentivity

Raw Coal Quality and Washability

Data

Selection of Mining Horizon Structural and Quality Modelling

Definition of Geological Resource

Blocks cut-off parameters

Geological Loss Domains

Resource Categories per Resource

Block

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Project Level and Resource Confidence

68%33%35%33%Average

20%0%20%80%> 20 years

70%10%60%30%15 to 20 years

80%50%30%20%Payback to 15 years

100%70%30%0%0 to Payback

M+IMeasuredIndicatedInferredPeriod

Pre-Feasibility Estimate

(Total Area Resource Data Confidence levels)

0%

10%

20%30%

40%

50%

60%

70%

80%

90%

100%

0 to Payback Payback to 15

years

15 to 20 years > 20 years Average

Inferred

Indicated

Measured

50%8%43%50%Average

0%0%0%100%> 20 years

40%0%40%60%15 to 20 years

70%10%60%30%Payback to 15 years

90%20%70%10%0 to Payback

M+IMeasuredIndicatedInferredPeriod

25%Average

0%> 20 years

10%15 to 20 years

40%Payback to 15 years

50%0 to Payback

MeasuredPeriod

Increase in measured % from Pre-Feasibility to

Feasibility

Measured and Indicated

0%

20%

40%

60%

80%

100%

0 to

Payback

Payback to

15 years

15 to 20

years

> 20 years Average

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Coal Resource Statement

Mineable Tonnes In-Situ (MTIS)

1. Includes the coal seam at the theoretical mining height and

between the relevant minimum and maximum mining heights

and coal quality cut-offs.

2. Includes dilution, but excludes contamination.

3. Geological loss factors shall be applied.

4. Tonnage quoted on a mineable in situ basis and coal quality

reported on an in situ bed moisture or air-dried

uncontaminated basis over the theoretical mining height.

5. May be subdivided into different depth and thicknesscategories.

MTISis reported per Resource Category(measured, indicated, inferred)

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GEOLOGICAL MODEL

BUILDING

GEOLOGICAL DATA

EVALUATION

REPORTED COAL

RESOURCES

Coal Resources per


GEOLOGICAL MODEL

OPTIMISATIONAnalysis of data integrity,

distribution, spatial data density

and confidence classification for

both Structural and Coal Quality

Data Sets

Confidence Classification into

Measured, Indicated and Inferred

Categories for both structural

and coal quality models

CLASSIFICATION

Sub-seam definition,

correlation and structural

interpretation

ROM DISCOUNT FACTORS

SALES DISCOUNT FACTORS

MINING AND SALES

DISCOUNT FACTORS

REPORTED COAL

RESERVES

Coal Reserves per


Classification into

Proven and Probable Reserve

Categories

CLASSIFICATION

LIFE OF MINE PLANMine Planning, Layouts,

Schedule = Economic Life of

Mine Model

MTIS

Application of Geological cut-

offs and losses

RESOURCE AND RESERVE ESTIMATION

Geological Domains,

Mining Horizon selection,

Optimisation of Interpolators

Confirmation of the estimated

discount factors from GTIS to

ROM and SALES within the

area mined

RECONCILIATION

TECHNICAL

MODIFYING FACTORSENVIROMENTAL, LEGAL

GOVERNMENTAL, ECONOMIC

INPUTS

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Run of Mine Coal Reserves

Extractable Coal Reserve =Mineable Tonnes In-Situ Resource

- Layout Loss and Barrier Pillar Loss(resources outside the mine layout)

x In-Panel Extraction %(mining method, safety factors)

- Mining Loss

Run of Mine (ROM) Coal Reserve =

Extractable Coal Reserve

+ Contamination(added as a thickness or a percentage)

+ ROM Moisture Correction Factor(surface moisture)

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Saleable Coal Reserves

Coal Product Interpretation Single stage or double stage wash

Max and Min washing densities

Relevant washtables (borehole, 25-0.5mm)

Product specs

Theoretical Practical Yield contamination borehole correlation factor (liberation) plant efficiency

Loss of fines

Beneficiation of fines coarse fines to spirals

spirals yield and efficeincy

ultra fines to floatation floatation yield and efficiency

Fines added back to Product / Discard

Effect of blending optimal cut-point density

Product moisture correction factor

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Saleable Coal Reserves

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Reporting of Reserves

Run of Mine Reserves (ROM)1. Tonnes are reported on a wet, contaminated basis

2. Coal qualities may be reported as wet, contaminated or air-dried,

contaminated or dry, contaminated

3. The basis for reporting coal qualities must be stated

Saleable Reserves

1. Product tonnage is reported on a wet product basis (as delivered)

2. Product qualities may be reported as wet product or air-dried product or

dry product

3. The basis for reporting product qualities must be stated

ROM and Saleable are reported per Reserve Category(proven, probable)

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Reconciliation

To Define the short term Budget Plan with a high degree of

confidence to ensure that we achieve budget tonnes, yield andrevenue predictions.

To Define the Operational Life of Mine Plan with a moderate to

high degree of confidence to ensure that we maximise reserve

utilisation and optimise expected financial returns.

To Define the new Project Life Of Mine Plan parameters with a

high degree of confidence to reduce the risk of not making the

expected financial returns.

Why do we do Reconciliations?

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Reconciliation - Operations

GEOLOGICAL MODEL

MINEABLE TONS IN-SITU

With Geol Loss, min and maxmining heights and coal qualityCut-offs applied

GEOLOGICAL FACTORS

Structural Model

Quality Model

Resource Confidence

Geol Loss

ROM FACTORS

Mining Loss, Dilution,Mining Recovery,Contamination andMoisture Adjustment

SALES FACTORS

Wash, Yield, Plant

Factor, fines andMoisture Adjustments

SALES MODEL

ROM MODELFINAL BUDGET MODEL

SURVEY AND

PRODUCTION DATA

Volumes

ROM Tonnes

Sales Tonnes

Yields and QualitiesMoisture Adjustment

MASS AND QUALITY BALANCES

Volume, Tons, Coal Quality

VARIANCE ANALYSIS

Identify differences betweenthe predicted model and actualproduction values

3

4

5

FACTOR

ADJUSTMENTS

RECONCILIATION OF OPERATIONAL FACTORS

SCHEDULEDRESERVES

Geological Factors

Mining Factors

ROM Tonnes

Sales Tonnes

Moisture Adjustment

1 2

3

FINANCIAL MODEL

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Reconciliation - Projects

What is different in the Project geology that can impactnegatively on the Project;

What is different in the mineability and operability that can

impact negatively on the Project;

Are the production rates realistically based on the deposit

geology?

Are the factors used in the Project evaluation similar to knownoperations, and should they be?

Are the factors used in the Project realistic and achievable?

Does the original view of the project turn out to be the reality in

the mine.

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Project Risk

1. Project factors can be significantly over- or under-estimated,

encouraging imprudent investment (corrected only with

difficulty)

2. Uncertainty is best predicted as a probabilistic range, not a

single deterministic forecast (this also helps geotechnical

evaluations and improves predictions)

3. The probability range is usually much greater than most

geoscientists believe their predictive ranges are too narrow.

4. Most statistical distributions are not normal (mean??)

Exploration expenditure is often described as a gamble where thescience of geology is the only factor which does not make it an

outright lottery

Why Should we Consider Monte Carlo Simulations?

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Project Risk

Common Distributions

Normal (Gaussian)commonly assumed

average valuecentral limits theorem

can result in < zero values

LogNormalmore realisticno < zero values

difficult statistics

Convert data to Ln values

Pertmaximum-minimum-expected

non-natural parameters

Better than triangular distribution

Beta

Beta (generalised)

Beta (subjective)

Binomial

Chi-Squared

Cumulative

DiscreteError

Erlang

Exponential

Extreme Value

Gamma

General

Geometric

Histogram

HypergeometricInteger Uniform

Inverse Gaussian

Logistic

Log-Logistic

Lognormal

Negative Binomial

Normal

Pareto

Pearson Type V, VIPert (Beta)

Poisson

Rayleigh

Students T

Triangular

Uniform

Weibull

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Project Risk

Element Distributions

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Project Risk

Prediction of Financial Performance

What is the

probability of

losing money on

this project?

8%

What is the

probability of

making more thanR 300 m?

33%

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