Finding the Weak Spots Quickly
Due Diligence Reviews of Mineral Resource Estimates
Peter Ravenscroft, FAusIMM
Burgundy Min...
Outline
• Background to Due Diligence process
• Finding the weak spots quickly
– Key value drivers
– Accuracy and Precisio...
Due Diligence
Definition

An investigation or audit of a potential
investment. Due diligence serves to confirm
all materia...
Review of Mineral Resource Estimates
Typically 2-3
years’ of work

Vast volumes
of information

Rapid assimilation, analys...
How to Find the Weak Spots Quickly
• Top-down focus on value drivers
• Recognise sources of potential Inaccuracy and Impre...
Drivers of Project Value
Project Value
(NPV)

Annual
Revenue

Metal/Produc
t Produced

Tonnes

Volume

×

×
Density

Explo...
Impact of Any Deficiencies
Accurate

Inaccurate

Precise

Imprecise

Accuracy and Precision
• Inaccuracy is a source or er...
Using the JORC Code as a Framework
The JORC Code provides a useful crossreference and framework for evaluating
resource es...
JORC Table 1 – Section 1
Criteria

Potential to Introduce Bias

Potential to Introduce Uncertainty

Sampling techniques

r...
JORC Table 1 – Section 3
Criteria

Potential to Introduce Bias

Database integrity
Site visits

Systematic errors in data
...
Estimation and Modelling Techniques
Paraphrased JORC Description*

Comments

nature and appropriateness of the estimation
...
Summary of Sources of Error
Volume
High
Priority
Issues

• Geological intepretation
• Data spacing and
distribution
• Orie...
Summary of Sources of Uncertainty
Volume
High
Priority
Issues

• Data spacing and
distribution
• Geological interpretation...
Finding the Weak Spots Quickly
DON’T:

DO:

• Try to read everything
• Get distracted by
insignificant detail
• Lose sight...
Peter Ravenscroft
Tel: +1-646-374-2429
peter.ravenscroft@burgundymining.com

www.burgundymining.com

Exploration, Resource...
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Due diligence reviews of mineral resource estimates

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Due diligence reviews of mineral resource estimates

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

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