Due diligence reviews of mineral resource estimates

Exploration, Resource & Mining Geology Conference 2013 Slide 1Exploration, Resource & Mining Geology Conference 2013 Slide 1

Finding the Weak Spots QuicklyDue Diligence Reviews of Mineral Resource Estimates

Peter Ravenscroft, FAusIMMBurgundy Mining Advisors LtdNassau, Bahamas

Exploration, Resource & Mining Geology Conference 2013 Slide 2

• 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

Outline


Due Diligence

DefinitionAn 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 assessmentof large amounts of complex

information


Review of Mineral Resource Estimates

Typically 2-3 years’ of work

Vast volumes of information

Definitive view on Value, Risk and Opportunity to support $$$ Bn decision

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?


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


Drivers of Project Value

Project Value(NPV)

Other Deductions

Annual Costs (capex, opex)

Annual Revenue

Metal/Product

ProducedPrice

Tonnes Recovered Grade

DensityVolume In-Situ Grade Recovery Factors

− −

×

×

× ×

A simplistic view that highlights areas of focus

Other Deductions

Annual Costs (capex, opex)

Price


Accurate Inaccurate

Precise

Imprecise

Impact of Any Deficiencies

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


Using the JORC Code as a Framework

The JORC Code provides a useful cross-reference 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


Criteria Potential to Introduce Bias Potential to Introduce UncertaintySampling 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

potential loss of coarse/fines sample size effects quality control and representivity

Quality of assay data and laboratory tests

quality control and representivity

quality control and representivity often negated by large N effect

Verification of sampling and assaying

control checks reduce risk of error

control checks reduce risk of variability

Location of data points impact on geological modelling volume estimation

Data spacing and distribution potential for over-sampling of high/low grade areas

need for coverage of all geological units

impact on resource classification

Orientation of data in relation to geological structure

potential for biased sampling errors in geological model and volume

estimates

Sample security confidence in sample/assay accuracy without contamination/tampering

Audits or reviews adds confidence to due diligence process adds confidence to due diligence process

JORC Table 1 – Section 1

Criteria Potential to Introduce Bias Potential to Introduce UncertaintySampling 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

potential loss of coarse/fines sample size effects quality control and representivity

Quality of assay data and laboratory tests

quality control and representivity

quality control and representivity often negated by large N effect

Verification of sampling and assaying

control checks reduce risk of error

control checks reduce risk of variability

Location of data points impact on geological modelling volume estimation

Data spacing and distribution potential for over-sampling of high/low grade areas

need for coverage of all geological units

impact on resource classification

Orientation of data in relation to geological structure

potential for biased sampling errors in geological model and volume

estimates

Sample security confidence in sample/assay accuracy without contamination/tampering



Criteria Potential to Introduce Bias Potential to Introduce Uncertainty

Database integrity Systematic errors in data Random errors in data

Site visits adds confidence to due diligence process adds confidence to due diligence process

Geological interpretation Fundamental to volume estimation Critical controls on density and grade

estimation• Inadequate geological interpretation adds

uncertainty

Dimensions Fundamental to volume estimation

Estimation and modelling techniques

• Overbearing impact on grade estimation• May drive volume and density estimation

• Can reduce uncertainty in estimates• Uncertainty characterisation for resource

classification

Moisture • Density (hence tonnage) estimation

Cut-off parameters • Drives volume and grade estimates

Mining factors or assumptions • Impact on volume and grade estimates • Uncertainty around assumptions made

Metallurgical factors/assumptions • Potential error in assumptions made • Uncertainty around assumptions made

Environmental factors/assmptionsBulk density • Fundamental source of error and bias

Classification • Valuation usually confined to Measured and Indicated • Defines level of uncertainty


Discussion of relative accuracy /confidence

• Provides measures of confidence, and potential for opportunity or risk

Criteria Potential to Introduce Bias Potential to Introduce Uncertainty

Database integrity Systematic errors in data Random errors in data

Site visits adds confidence to due diligence process adds confidence to due diligence process

Geological interpretation Fundamental to volume estimation Critical controls on density and grade

estimation• Inadequate geological interpretation adds

uncertainty

Dimensions Fundamental to volume estimation


• Overbearing impact on grade estimation• May drive volume and density estimation

• Can reduce uncertainty in estimates• Uncertainty characterisation for resource

classification

Moisture • Density (hence tonnage) estimation

Cut-off parameters • Drives volume and grade estimates

Mining factors or assumptions • Impact on volume and grade estimates • Uncertainty around assumptions made

Metallurgical factors/assumptions • Potential error in assumptions made • Uncertainty around assumptions made

Environmental factors/assmptionsBulk density • Fundamental source of error and bias

Classification • Valuation usually confined to Measured and Indicated • Defines level of uncertainty


Discussion of relative accuracy /confidence

• Provides measures of confidence, and potential for opportunity or risk

JORC Table 1 – Section 3


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 methodology must be appropriate to style of deposit and data available

Domaining can have critical impact on volume, density and grade estimates

Interpolation parameters are often a weakness – inappropriate search parameters

block size in relation to the average sample spacing and the search employed.

Unrealistic block sizes are commonly used and introduce bias and inappropriate apparent precision

assumptions behind modelling of selective mining units.

Recoverable resource estimation critical where selective mining above cut-off grade is to be used

description of how the geological interpretation was used to control the resource estimates.

Fundamental control on estimation

process of validation, the checking process used, the comparison of model data to drill hole data, and use of reconciliation data if available.

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


Summary of Sources of Error

Volume Density Grade

High PriorityIssues

• Geological intepretation• Data spacing and

distribution• Orientation with respect to

geology• Dimensions• Cut-off parameters• Classification

• 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

• Estimation and modelling techniques

• Geological interpretation• Data collection


• 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 OrderIssues

• Geological logging• Mining factors or

assumptions

• Quality of assay data and laboratory tests

• Mining factors or assumptions

• Quality of assay data and laboratory tests


Note: Each of these elements is described in more detail in JORC Table 1


Summary of Sources of Uncertainty

Volume Density Grade

High PriorityIssues


• Geological interpretation• Relative

accuracy/confidence

• Relative accuracy/confidence

• Relative accuracy/confidence

Second OrderIssues


• 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


Finding the Weak Spots Quickly

DON’T:• Try to read everything• Get distracted by

insignificant detail• Lose sight of the likely

economic impact of any issue

DO:• 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 15Exploration, Resource & Mining Geology Conference 2013 Slide 15

Peter RavenscroftTel: +1-646-374-2429

[email protected]

www.burgundymining.com

mailto:[email protected]

Technology

Due diligence reviews of mineral resource estimates