1. Finding the Weak Spots Quickly
Due Diligence Reviews of Mineral Resource Estimates
Peter Ravenscroft, FAusIMM
Burgundy Mining Advisors Ltd
Nassau, Bahamas
Exploration, Resource & Mining Geology Conference 2013
Slide 1

2. Outline
• Background to Due Diligence process
• Finding the weak spots quickly
– Key value drivers
– Accuracy and Precision
– Framework from JORC Table 1
• Summary of key issues
Exploration, Resource & Mining Geology Conference 2013
Slide 2

3. Due Diligence
Definition
An investigation or audit of a potential
investment. Due diligence serves to confirm
all material facts in regards to a sale.
Objectives
Assess value, risks and opportunities.
Process
• Assembly of multi-disciplinary team
• Access to comprehensive data room
• Site visits and Q&As
Requirement for rapid assessment
of large amounts of complex
information
Exploration, Resource & Mining Geology Conference 2013
Slide 3

4. Review of Mineral Resource Estimates
Typically 2-3
years’ of work
Vast volumes
of information
Rapid assimilation, analysis
and reporting of outcomes,
often in 2-3 days
How can we reach a
robust, reliable result in
such a short time frame?
Definitive view on Value, Risk and Opportunity
to support $$$ Bn decision
Exploration, Resource & Mining Geology Conference 2013
Slide 4

5. How to Find the Weak Spots Quickly
• Top-down focus on value drivers
• Recognise sources of potential Inaccuracy and Imprecision
• Use the JORC Code Table 1 as a reference framework
Stay out of the weeds and
resist all temptations to
go down rabbit holes
Exploration, Resource & Mining Geology Conference 2013
Slide 5

6. Drivers of Project Value
Project Value
(NPV)
Annual
Revenue
Metal/Produc
t Produced
Tonnes
Volume
×
×
Density
Exploration, Resource & Mining Geology Conference 2013
−
×
Annual Costs
Annual Costs
(capex, opex)
(capex, opex)
Other
Deductions
−
Price
A simplistic view that
highlights areas of focus
Recovered
Grade
In-Situ Grade
×
Recovery
Factors
Slide 6

7. Impact of Any Deficiencies
Accurate
Inaccurate
Precise
Imprecise
Accuracy and Precision
• Inaccuracy is a source or error or
bias, and can lead to under- or
over-valuation of the asset
• Imprecision is a source of
uncertainty, and introduces
downside risk or upside
opportunity
Materiality
• Commonly a limit of materiality is defined for the due diligence – eg issues
having an NPV impact of less than $xxM are not pursued
• This avoids unnecessary effort on insignificant issues
Exploration, Resource & Mining Geology Conference 2013
Slide 7

8. Using the JORC Code as a Framework
The JORC Code provides a useful crossreference and framework for evaluating
resource estimates
• Although an Australasian Code it is
widely used internationally
• All resource geologists are familiar with
its contents
Table 1 provides a comprehensive checklist
for the elements that must be considered
in preparing Pubic Reports
• Section 1 covers Sampling Techniques
and Data
• Section 3 relates to Estimation and
Reporting of Mineral Resources
Exploration, Resource & Mining Geology Conference 2013
Slide 8

9. JORC Table 1 – Section 1
Criteria
Potential to Introduce Bias
Potential to Introduce Uncertainty
Sampling techniques
representivity
calibration of tools and systems
sample size
repeatibility
Drilling techniques
core vs RC etc
core diameter, triple tube etc
core vs RC etc
sample accuracy
Drill sample recovery
representivity
preferential loss/gain of coarse/fine material
variability and repeatibility
Logging
impact on accuracy of geological modelling
impact on precision of geological modelling
Sub-sampling techniques and
sample preparation
Quality of assay data and
laboratory tests
Verification of sampling and
assaying
Location of data points
potential loss of coarse/fines
sample size effects
quality control and representivity
quality control and representivity
quality control and representivity
often negated by large N effect
control checks reduce risk of error
control checks reduce risk of variability
Data spacing and distribution
potential for over-sampling of high/low
grade areas
need for coverage of all geological units
potential for biased sampling
errors in geological model and volume
estimates
confidence in sample/assay accuracy
without contamination/tampering
impact on resource classification
adds confidence to due diligence process
adds confidence to due diligence process
Orientation of data in relation to
geological structure
Sample security
Audits or reviews
impact on geological modelling
volume estimation
Exploration, Resource & Mining Geology Conference 2013
Slide 9

10. JORC Table 1 – Section 3
Criteria
Potential to Introduce Bias
Database integrity
Site visits
Systematic errors in data
Random errors in data
adds confidence to due diligence process
adds confidence to due diligence process
Fundamental to volume estimation
Critical controls on density and grade
estimation
Geological interpretation
Dimensions
Estimation and modelling
techniques
Moisture
Potential to Introduce Uncertainty
•
Inadequate geological interpretation adds
uncertainty
•
•
Can reduce uncertainty in estimates
Uncertainty characterisation for resource
classification
Fundamental to volume estimation
•
•
Overbearing impact on grade estimation
May drive volume and density estimation
•
Density (hence tonnage) estimation
Cut-off parameters
Mining factors or assumptions
Metallurgical factors/assumptions
Environmental factors/assmptions
•
Drives volume and grade estimates
•
Impact on volume and grade estimates
•
Uncertainty around assumptions made
•
Potential error in assumptions made
•
Uncertainty around assumptions made
Bulk density
•
Fundamental source of error and bias
•
Valuation usually confined to Measured and
Indicated
•
Defines level of uncertainty
Classification
Audits or reviews
Discussion of relative accuracy
/confidence
adds confidence to due diligence process
Exploration, Resource & Mining Geology Conference 2013
adds confidence to due diligence process
•
Provides measures of confidence, and
potential for opportunity or risk
Slide 10

11. Estimation and Modelling Techniques
Paraphrased JORC Description*
Comments
nature and appropriateness of the estimation
technique
key assumptions, including treatment of extreme
grade values, domaining, interpolation parameters
and maximum distance of extrapolation from data
points.
Estimation
and
modelling
techniques
Estimation methodology must be appropriate to
style of deposit and data available
Domaining can have critical impact on volume,
density and grade estimates
block size in relation to the average sample spacing
and the search employed.
assumptions behind modelling of selective mining
units.
description of how the geological interpretation
was used to control the resource estimates.
process of validation, the checking process used,
the comparison of model data to drill hole data,
and use of reconciliation data if available.
Interpolation parameters are often a weakness –
inappropriate search parameters
Unrealistic block sizes are commonly used and
introduce bias and inappropriate apparent
precision
Recoverable resource estimation critical where
selective mining above cut-off grade is to be used
Fundamental control on estimation
In properties with current or historical production,
reconciliation often provides the key to accuracy
and precision of the model
* Note this represents a shortened extract from Table 2, highlighting the author’s opinion of the most important aspects
Exploration, Resource & Mining Geology Conference 2013
Slide 11

12. Summary of Sources of Error
Volume
High
Priority
Issues
• Geological intepretation
• Data spacing and
distribution
• Orientation with respect to
geology
• Dimensions
• Cut-off parameters
• Classification
Density
• Geological interpretation
• Moisture
• Estimation and modelling
techniques
• Data collection
• Data spacing and
distribution
• Sample preparation
• Sampling techniques
• Drilling techniques
• Sample recovery
• Location of data points
Grade
• Estimation and modelling
techniques
• Geological interpretation
• Data collection
• Data spacing and
distribution
• Location of data points
• Drilling techniques
• Sampling techniques
• Sample recovery
• Sample preparation
• Orientation of data in
relation to geological
structure
• Sample security
• Cut-off parameters
Second
Order
Issues
• Geological logging
• Mining factors or
assumptions
• Quality of assay data and
laboratory tests
• Mining factors or
assumptions
• Quality of assay data and
laboratory tests
• Mining factors or
assumptions
Note: Each of these elements is described in more detail in JORC Table 1
Exploration, Resource & Mining Geology Conference 2013
Slide 12

13. Summary of Sources of Uncertainty
Volume
High
Priority
Issues
• Data spacing and
distribution
• Geological interpretation
• Relative
accuracy/confidence
Second
Order
Issues
Density
•
• Mining factors or
assumptions
Relative
accuracy/confidence
Grade
•
Relative
accuracy/confidence
• Location of data points
• Data spacing and
distribution
• Sampling techniques
• Drilling techniques
• Sample recovery
• Sample preparation
• Quality of assay data and
laboratory tests
Note: Each of these elements is described in more detail in JORC Table 1
Exploration, Resource & Mining Geology Conference 2013
Slide 13

14. Finding the Weak Spots Quickly
DON’T:
DO:
• Try to read everything
• Get distracted by
insignificant detail
• Lose sight of the likely
economic impact of any
issue
• Use a top-down, high
level approach
• Focus on the key value
drivers of Volume,
Density and Grade
• Follow a structured
framework
BUT REMEMBER:
• Your conclusions may underpin a multi-billion dollar
investment and need to be clear, justified and defensible
Exploration, Resource & Mining Geology Conference 2013
Slide 14

15. Peter Ravenscroft
Tel: +1-646-374-2429
peter.ravenscroft@burgundymining.com
www.burgundymining.com
Exploration, Resource & Mining Geology Conference 2013
Slide 15