Estimating Coal Resources: The

Reporting Codes in Motion

Botswana Coal & Energy 2013

Herman Dorland

Introduction

1. Scope of the SAMREC code

2. Principles

– Materiality

– Transparency

– Competency

• Not unduly influenced

3. Confidence.

Scope of the SAMREC Code – SAMREC 2007

• “The Code sets out a required minimum standard for the Public Reporting of Exploration Results, Mineral Resources and Mineral Reserves. References in the Code to Public Report or Public Reporting pertain to those reports detailing Exploration Results, Mineral Resources and Mineral Reserves and prepared as information for investors or potential investors and their advisors.”

Why do we need Coal Resource estimations?

• Project financial viability

• Project financing

• Mine Planning and Production Scheduling

• Assist with wash-plant design

• Contract negotiations

– Service providers

– Customers

• Mine and operations management

• Reconciliation

Resource estimation process flow chart

assess data

quality

geological

interpretation

code and

composite data

top cutting

strategies

variography

parameter

optimisation

build block

model

define search

neighbourhood

estimation

density

modelling

validation

classification

and reporting

report writing

sign off

resource to

reserve

handover

external

auditing

reconciliation

drilling

sampling

logging &

mapping

assaying

plan drilling

assess

database

integrity

statistical

analysis and

domaining

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Basic Coal Resource estimation process

SAMREC Code Principles - Materiality

• “A Public Report contains all the relevant information that investors and their professional advisors would reasonably require, and expect to find, for the purpose of making a reasoned and balanced judgement regarding the Exploration Results, Mineral Resources and Mineral Reserves being reported on.”

Structural Components of the Coal Resource Model

Weathered

Fresh

Quality Components of the Coal Resource Model

Falcon, R.M.S (1986)

What about Washability?

SAMREC Code Principles - Transparency

• “A reader of a Public Report must be provided with sufficient information, the presentation of which is clear and unambiguous, to understand the report and not be misled.”

Database and Validation

• Collar positions – Use of DTM or modelled surface using collar

elevation value

• Geological logging (all relevant geological aspects covered, readable)

• Overlapping/missing intervals and incorrect stratigraphy

• Ash - CV and Ash – RD relationship (Moisture basis)

• Proximate analyses adds up to 100%

• Higher ash related to lower CV in washability analyses

Validation

unreadable and/or adjusted paper logs

QAQC

• Calibration of analytical equipment – Standards and Certified Reference Materials. Low and high value material must be used to calibrate analytical equipment

• Differences between Duplicate Samples

– Field Duplicates (core coarsely crushed and sample divided)

– Laboratory Duplicates (duplicate sample of pulverized sample material)

• Historical data versus modern data

Precision, Accuracy and Bias

precise

accurate

unbiased

imprecise

accurate

unbiased

precise

inaccurate

biased

imprecise

inaccurate

biased

Statistical Analysis

• Selective horizons and mining horizon composites

• Declustering – Well drilled are close to current mining versus less well drilled

area in the life of mine area

• Population characteristics (mixed populations, normal versus skewed). For example, raw CV, raw sulphur and product cut-point density will display different population characteristics

• Coefficient of variance (for example product yields for a low ash product may have a higher Coefficient of variance than a higher ash product out of the sample washability analyses)

• Domaining (low volatile matter areas near dolerite, versus higher volatile matter areas where coal is not affected by dolerite)

Population Distribution

normal

mean=median=mode

positive skew

mean>median>mode

negative skew

mean<median<mode

single population mixed populations

Validation

• Visual validation (contours and sections)

• Global mean validation (The mean of the

estimates should be similar to the mean of the

input composites)

• Population distribution validation (compare

histograms of input composites and estimates)

Population Distribution Validation

quality

frequency (

%)

model 1

ordinary kriged

estimate

quality

frequency (

%)

input

composites

quality

frequency (

%)

model 2

indicator kriged

estimate

Coefficient of Variance

• A measure of dispersion of the probability

distribution

– CV = Standard deviation ÷ mean

• Empirical upper limit where a Normal

Distribution may occur CV=1.5

Review and audits

• Peer review

• Audits

• Technical review

• Expert opinion

SAMREC Code Principles - Competency

• “The Public Report is based on work that is the responsibility of suitably qualified and experienced persons who are subject to an enforceable Professional Code of Ethics.”

Competent Person Definition – SAMREC 2007

• “A ‘Competent Person’ is a person who is registered with SACNASP, ECSA or PLATO, or is a Member or Fellow of SAIMM, the GSSA or a Recognised Overseas Professional Organisation (ROPO). A complete list of recognized organisations will be promulgated by the SSC from time to time. The Competent Person must comply with the provision of the relevant promulgated Acts.”

Competent Person Experience - SAMREC 2007

• “A Competent Person must have a minimum of five years’ experience relevant to the style of mineralization and type of deposit or class of deposit under consideration and to the activity he or she is undertaking. If the Competent Person is estimating or supervising the estimation of Mineral Resources, the relevant experience must be in the estimation, assessment and evaluation of Mineral Resources. If the Competent Person is estimating, or supervising the estimation of Mineral Reserves, the relevant experience must be in estimation, assessment, evaluation and assessment of the economic extraction of Mineral Reserves. Persons being called upon to sign as a Competent Person must be clearly satisfied in their own minds that they are able to face their peers and demonstrate competence in the commodity, type of deposit and situation under consideration.”

Competent Person Responsibility – SAMREC 2007

• “The lead Competent Person undertaking Mineral Resource reporting should accept full responsibility for the report and should not treat the procedure merely as a ‘rubber-stamping’ exercise.”

Not Unduly Influenced – SAMREC 2007

• “The author off the Public Report should be satisfied that: his work has not been unduly influenced by the organization, company pr person commissioning a report or any report that may be deemed a Public Report; all assumptions are documented; and adequate disclosure is made of all material aspects that the informed reader may require in order to make a reasonable and balanced judgement thereof.”

Resource Confidence – SANS 1032:2004

• Measured-”part of a coal resource for which tonnage, densities, shape, physical characteristics, and coal quality can be estimated with a high level of confidence. It is based on detailed and reliable exploration, sampling and testing information gathered through appropriate techniques from locations such as outcrops, trenches, pits, workings and boreholes. The locations are spaced closely enough to confirm physical continuity and coal quality continuity.”

• NOTE A measured coal resource is defined by coal in the full seam above a minimum thickness cut-off and relevant coal quality cut-offs, as defined by the competent person, which meets the criteria for reasonable and realistic extraction.

Resource Confidence – SANS 1032:2004

• Indicated-“part of a coal resource for which tonnage, densities, shape, physical characteristics and coal quality can be estimated with a moderate level of confidence. It is based on exploration, sampling and testing information gathered through appropriate techniques from locations such as outcrops, trenches, pits, workings and boreholes. The locations are appropriate to confirm physical continuity, while the locations are too widely or inappropriately spaced to confirm coal quality continuity. However, such locations are spaced closely enough for coal quality to be assumed.”

• NOTE An indicated coal resource is defined by coal in the full seam above a minimum thickness cut-off and relevant coal quality cut-offs, as defined by the competent person, which meets the criteria for reasonable and realistic extraction .

Resource Confidence – SANS 1032:2004

• Inferred-”part of a coal resource for which tonnage, densities, shape, physical characteristics and coal quality can be estimated with a low level of confidence. The resource is inferred from geological evidence and assumed, but not verified physical continuity with or without coal quality continuity. It is based on exploration, sampling and testing information gathered through appropriate techniques from locations such as outcrops, trenches, pits, workings and boreholes which are limited or of uncertain quality and reliability.”

• NOTE An inferred coal resource is defined by coal in the full seam above a minimum thickness cut-off and relevant coal quality cut-offs, as defined by the competent person, which meets the criteria for reasonable and realistic extraction .

Spatial Analysis

• Short spaced variability (the Nugget Effect; Co)

• Long spaced variability (borehole distance

>350m)

• Directionality - anisotropy

Variogram vari

ogra

m

(h)

sample separation (h)

sill

range

sample 1

sample 1

sample 1

sam

ple

2

sam

ple

2

sam

ple

2

Using the Variogram (thickness, quality and yield) as Guide to Classification

h

vari

ogra

m

(h)

|

long

range

total sill

vari

ogra

m

(h)

vari

ogra

m

(h)

total sill

|

short

range

|

long

range

2/3 sill

h |

short

range

|

long

range

total sill

2/3 sill

h

Inferred Indicated Measured

What is a Mathematical Model?

• A model is a simplified representation of

reality, from which useful conclusions can be

drawn

Sampling

• Decisions worth millions of dollars are based

on sampling

• At a 350 m grid spacing we sample

approximately 0.00000004% of the Coal or 4

parts per 100 million with diamond core

• In the laboratory, only a small part of this

core sample is used for analyses (for

example 2 g of a 30 kg core sample are used

for a CV analysis)

500 m Spacing Versus 350 m Spacing

500 m spacing 350 m spacing

What is your case?

What is your case?

Reality Structure Model 1

Reality Structure Model 2

Reality Quality Model 2

What is your case?

Structure Quality

What is your case?

Structure Quality

Yield %

Cut Point Density

Resource Estimation

• Interpolation to estimate the value between

data points in a realistic fashion

• Inverse distance to a Power – weighting based

on separation distance from the point of

estimation - data points close to the point of

estimation weighs a lot

• Kriging- weights selected using variogram

model – BLUE Estimator- Best Linear Unbiased

Estimate

Estimation: we start with a few measured values

Measured Value

Interpolation: we estimate the rest of the values using a mathematical algorithm

Measured Value

Estimated Values

Other Factors Influencing Classification

• Data Confidence – The CP must trust the data

• Good looking Variograms can still result from poor quality data!

• Structural Interpretations

• A 500 m range for Measured (based on variography) is not appropriate in a structurally complex geological domain

• Assessment of BOTH physical and Coal Quality Continuity is required

Where is Coal Resource Estimation Used?

construction feasibility pre-

feasibility exploration discovery operations closure

INCREASING COMPLEXITY

SMALLER SCALE

Feasibility Geological Study - SANS 10320:2004

• “The feasibility geological study requires the cored borehole spacing to be reduced to an acceptable level to define the coal deposit as a measured coal resource or an indicated coal resource. The proportion of measured coal resources should at least cover the five-year mining window or the payback area, whichever is the longer.”

Impact on Business: Good and Bad estimation

Process Good Poor

Logging Mining model flexible Mining model inflexible

Sampling Mining model close to reality Mining model far from reality

QAQC Confidence in Resource estimate

Distrust of Resource estimate

Statistics Understanding of impact of structural and quality data on estimate

Data not understood

Spatial analysis Impact of variability in thickness, yield, cut-point density and sulphur known. Understanding of influence of these factors on mining and wash-plant design and product contract

Variability not known. Inappropriate mine and plant design and product contract

Impact on Business: Good and Bad Estimation

Process Good Poor

Coal Resource Estimation

Project worth close to reality, correct investment decision

Project worth far from reality, incorrect investment decision

Validation Model represents input data Model does not represent input data

Classification All factors are taken into account and confidence in estimate and what may be expected during mining is well understood

Important factors influencing the confidence in the Resource estimate is not taken into account leading to unexpected problems during mining and in the wash-plant

Reporting Well informed investment, design and operational decisions are made

Decisions are poorly informed

Conclusion

1. The SAMREC Code sets out a required minimum

standard for the Public Reporting of Coal Resources

2. The principles of the SAMREC Code should be reflected in the Coal Resource estimation process

3. Coal Resource confidence is related to the entire Resource estimation process where every step plays an important role in the estimation confidence.

Questions?