anglo coal
TRANSCRIPT
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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 Category
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
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 Category
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%