How New Due Diligence is Transforming Mining Operations

"The Bankers are Turning Nasty"
How new Due Diligence requirements are
changing design and scope of mining operations
Simon Michaux
April 2013

Mining is about making money
• Mining cannot function without investor finance
• The investor finance sector has had a low opinion of mining
for the last few decades. This opinion has degraded
substantially in the last 10 years
• The most voracious complaint is that promised engineering
targets are not met in the agreed time frame
• A company that doesn’t deliver on the agreed terms in its
contract has its reputation burned in investment circles
• Banks think in terms of “if they don’t deliver on their
contracts we can take over their operation…”

Global demand for mining resources can
be tracked with steel consumption
Steel consumption is a good proxy for industrialisation
Controlled by economic crashes and geopolitical events

China is dominating the rest of the planet
China now dominates manufacturing and resource consumption

Why have the last 5 years in particular
been very difficult?
• Things have gotten really difficult for mining operations to get
investment since 2008 (GFC)
• It has been really hard to get available credit from banks since
they themselves have been put under pressure to be solvent
• You could have a robust business case shown in a FS that pre-
2008 would have easily attracted investment, but banks won’t
touch it
• There are now structural volatility risks that did not exist 15
years ago (e.g. sovereign debt default or a credit freeze)

Basis for CAPEX credit loan
• Based on company reputation
• Based on the business plan. If it is a clear one with easy
milestones then its considered low risk
• Tier Ones still need to get finance like everyone else from
time to time
• In the 1980’s and 1990’s project based finance was the usual
way to get a mine operation funded
• Since 2008, this has gone out of favour and now company
based finance is more common
• Companies strong enough might issue bonds

Bankers really do rule the world!
• Plan the flight
• Then fly the plan
• Or get your money from
someone else
Mining
Company
Finance
Investors
Mine
Operation
These guys couldn’t care
less about technical
problems, they want their
money with a very ruthless
hard nosed attitude
Authority
Pressure

Investor confidence in the mining industry
is becoming strained
0
200
400
600
800
1000
1200
1400
1600
1800
CashCosts($/oz)
Gold Producers -
(what they when raising captial to fund venture)
Reported cash costs ($/oz)
0
200
400
600
800
1000
1200
1400
1600
1800
CashCosts($/oz)
Gold Producers -
(what they said vs. what they did)
Reported cash costs ($/oz)
Estimated total cash costs ($/oz)
Source: Bell Potter 2011

An average blowout of 54% of original cost
of production estimate
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Percentoforginigalestimate(%)
Cost of Producing Au (per oz) Blowout
Investors are starting to become more street savvy with all this

Why did this happen?
• Underestimating costs
• Overestimating revenue
• Underestimating mining schedule
• Underestimating metal price
• Not accounting for certain risks
• Wearing rose tinted glasses when
signing off

Busang – the largest mining fraud in history
• Thousands of investors were duped by the small Calgary-based
mining company (Bre-Ex) that falsely claimed to have struck gold
in Indonesia
• The estimate of the site's worth increased over time; in 1997 it
was 200 million troy ounces of gold
• At its peak it had a market capitalization equal to US$4.4 billion,
equal to US$6.3 billion in current terms adjusted for inflation
• Busang ore samples had been salted with gold dust
• Toronto Stock Exchange lost billions of dollars as a direct result

How did the investment sector respond?
• Banks would add 2-4% IRR on the hurdle rate
• Toronto Stock Exchange took action
– National Instrument 43-101 (the "NI 43-101" or the "NI")
– The NI is a strict guideline for how public companies can disclose
scientific and technical information about mineral projects on bourses
supervised by the Canadian Securities Administrators
– It requires a ‘qualified’ person to take personal responsibility for the
outcome, which if disproven will face fraud charges
• A standard used for the public disclosure of information
relating to mineral properties in Canada
• Most ventures now require this document to raise finance

What companies quote to share markets
• Royalties and mining taxes not included
• CAPEX cost (often just quote OPEX)
• Numbers quoted based on a particularly successful phase of
operation where grade is particularly high
• Value of whole deposit but quoting only first phase of design,
ignoring later expansion phases of construction
• Low discount rate for Discount Cash Flow (DCF) calculation
Remember the operation’s priority has been to get finance to start
Often what is quoted is missing important details

So what is missing and often not quoted?
• Royalties and mining taxes
• CAPEX cost (often just quote OPEX)
• Numbers quoted based on a particularly successful phase of
operation where grade is particularly high
• Value of whole deposit but quoting only first phase of design,
ignoring later expansion phases of construction
Remember the operation’s priority has been to get finance to start

Recent major mining project CAPEX overruns
Project
Company
Feasibility
budget cost
Actual/forecast
cost overrun
Ravensthorpe/Yabilu
Expansion
BHP Billiton A$1.4 billion 30%
Spence (Chile) BHP Billiton US$990 million 10%
Telfer Mine Newcrest A$1.19 billion 17.50%
Stanwell Magnesium AMC A$1.3 billion 30%
Boddington Newmont A$866 million 100%
Goro Project
(Indonesia)
Inco US$1.45 billion 15%
Prominent Hill Oxiana A$350 million 51%
Source: Noort and Adams 2006

Public-Private Project(PPP) Business Analysis
of Mining Project Ventures
• 86% of publicly procured projects had capital cost over runs
• The average CAPEX overrun was 28%
• These metrics had not changed in the prior 90 years
Bent Flyvberg (2002)
Project database of 258 projects drawn from a surveyed
population of 806 mining projects

Public-Private Project(PPP) Business Analysis
• The average CAPEX overrun was 22%
• There was no correlation between who complied the original
estimate
• Problems with in-house as well as ‘blue chip’/’highly reputed’
consultants
• Upper-tier operators were no better than ‘juniors’
• No influence from project size/location
• Bank consultants (due diligence audits) routinely failed to identify
‘red flags’ or CAPEX overruns
Chris Gypton (2002) - Project database of 60 projects drawn
from a surveyed population of 380 projects, over 21 years

Categories of study
• Mine completed on time?
• Mine completed at budget?
• Produced at design capacity?
• Producing expected cash flows?
Conclusions
• 12 of 18 had construction delays
– 5 more than 2 years late
– 2 never satisfied their ‘option’ test
• 12 of 18 had CAPEX overruns
– 10 mines had greater than 20%
• 14 of 18 had difficulty operating at
capacity
– 6 persisted for more than 3 years
• 12 of 18 yielded cash flow below original
estimate
– 3 mines never achieved budgeted operating
levels
– 5 had negative cash flows
– 6 had lower commodity prices
Gary Castle (1985) – Chemical Bank (now part of JP Morgan Chase)
Project database of 18 mining projects

A mine is a hole in the ground with…
• a liar at the bottom
• and a lawyer at the top
• a lawyer at the bottom
• and a liar at the top
Or worse
This is how the mining industry is viewed by finance
investors, upon which mining depends.

If this is the way of things for the last 90
years or so, why is a change in mining
practice being ‘suggested’ from the finance
sector now?

The big squeeze and technology solutionsTechnology – extraction
Andrew Mackenzie, Group Executive and Chief Executive Non-Ferrous Slide 25
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
0
5
10
15
20
25
30
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030
Cu production
Run of Mine grade
Flotation
Acidic leach, solvent
extraction,
electrowinning
Central Africa
Copper Belt peak
Copper production
(million tonnes per annum)
Run of mine grade
(Cu %)
Source: US Geological Survey(1900-83),Brook Hunt(1984onwards).
Bacterial leaching
Bulk open pit
mining
Flash furnace
Direct ore
Reverberatory
furnace
In pit crushing
The next technological paradigm change is needed now

Copper Heap Leach SX-EW Operation
• Study done to provide an
estimate of the grade required
to support an operation of a
given resource size
• Based on a 10 year mine life
• Typical reverse economics cost
curve at a 10% IRR
Mining OPEX is creeping up
and industry does not know why
(they must keep it under $10/tonne)

Typical Leaching recovery vs. deposit size
Comparison of proposed/existing
operations with typical reverse
economics cost curves

Economies of scale have carried the industry

Mining has gotten bigger
• Size of process plant in 2013
– Large 50 million tonnes/year
– Small 10 million tonnes/year
• Standard truck size has increased
– 1940 10 tonne truck
– 2013 300 tonne truck
• Installed grinding power has
increased
– 1940 ½ MW
– 2013 28MW
It all costs money

Mine Development,
2.1
Mine Dewatering, 0.2 Mine Building &
Services, 1.0 Contractor
Mob/demob, 1.0
Waste Dump
Rehab, 0.5
Crushing &
Screening, 2.5
Pond & solution
clarifiaction, 1.4
Cu SX, 1.5
Cu EW, 8.2
Reagents, 0.7
Laboratory, 1.5
Plant Buildings, 2.0
Plant
Services,
3.1
Plant Mobile
Equipment, 1.0Environment
and Rehab, 0.3
Site
Roads, 3.3
Service Roads and
Links, 2.0
Power and Water, 6.4Communications,
0.3
Airport, 0.5
Plant Control
System, 0.5
Accommodation &
Facilites, 3.9
Construction
Facilites, 3.0
EPCM, 7.4
Owner
Costs,
3.5
Typical Heap Leaching CAPEX distribution
Total CAPEX US$M 57.5
MINE
$US 4.8M
PROCESS PLANT
$US 15.9M
ADMIN
$US 4.7M
PLANT CONSTRUCTION
$US 12.7M

Contractor , 2.3
Pad, 0.7
Laboratory/Assayi
ng, 0.1
Environment and
Rehab, 0.2
Mine Salaries, 0.4
Water, 0.02 Areil mapping, 0.1
Supplies, 0.1
Computing, 0.1
Consultants, 0.2
Other, 0.2
Plant Salaries, 0.4
Maintenance, 0.8Plant Power &
Water, 2.2
Reagents,
1.4Head
Office/Admin, 0.5
Site
Salaries
, 1.3
FIFO tarvel, 1
Catering, 1.6
Site Power/Water
Services, 2.8
Typical Heap Leaching OPEX distribution
Total OPEX US$M 16.42
MINE
$US 4.42M
PROCESS PLANT
$US 4.8M
ADMIN
$US 7.2M

Economic goal posts are shifting for future
deposits
• Huge low grade deposits
• Penalty minerals more prominently present in deposit
that prevent efficient processing
• Ever decreasing grind sizes (close size 10-20mm)
• Operating on an economy of scale never been seen
before (4MT blasted rock a day, 60% of which is ore!)
• To stay economically viable, economics of scale have to
be applied. Operations will double and triple in size.
All of this based on the assumption that there is no energy or water shortage

The word from London…
NPV
CAPEX
If this ratio is too low, then the project
doesn’t start
Projects are paid for by net profit from high
grade parts of the deposit processed in the
short term.
There seems to be no Plan B if there are no
high grade parts!

So how did this happen?
Why should I give you my money?
What they are really asking:

The Phases of an Effective Mining Project
• Scoping study SS (eliminate phase)
– Accuracy +/-30-50%
• Prefeasibility study PFS (select phase)
• Definitive feasibility study FS (refine phase)
• Design and construction
• Operations

Often what is called Feasibility Study is misused
A feasibility study is a detailed study to determine the
economic variability of a project. Thus sometimes the
answer is NO, the project is not economically feasible.
Adding ‘bankable’ to the title does not guarantee it is
feasible, but merely dictates the level of accuracy.

Sample feasibility costs
Source: MacKenzie and Cusworth 2007
Operation Type
Project Estimated
Cost A$ M
Cost of Feasibility
study A$ M
Percentage of
total cost
Brownfields Smelter $197 $4.2 2.1%
Brownfields OP mine/refinery $235 $8.7 3.7%
Brownfields UG mine $250 $3.0 1.2%
Brownfields Mine/materials handling $593 $10.5 1.8%
Brownfields Smelter $680 $14.0 2.1%
Greenfields OP mine/concentrator $750 $12.9 1.7%
Greenfields OP mine/refinery/new technology $750 $23.0 3.1%
Greenfields OP mine/refinery/new technology $901 $12.7 1.4%
Greenfields OP mine/rail/port $1,950 $74.0 3.8%
Min 1.2%
Max 3.8%
Average All Projects 2.3%
Average Brownfields 2.2%
Average Greenfields 2.5%

The Leverage of Early Work

The ability to create or add value

Degree of definition in study phases

What is this thing called risk?
Risk = Hazard + Outrage
Risk = (Hazard*Outrage)P(Force Majeure)
Now
Perceived future model

Useless or vague risk descriptors
• Economic
• Business
• Project
• Development
• Elemental
• Global
• External
• Debt (servicing)
• Systemic/system
• Commercial
• Financial
• Construction
• Physical
• Competition
• Local
• Internal
• Bankers
• Network
Many proposals are written around this structure and the result
has been vague recommendations an uncertain financial risk

There are six standard due diligence
reports expected on every deal
• Reserves
• Engineering/technical
• Environmental
• Insurances
• Tax
• Accounting Sometimes combined
First steps are to be sure that the risk categories are clear and that
the overlaps of risks are thoroughly analysed in each report

Supply/inputs/orereserve
Market
Foreignexchange
Operating:technical
Operating:cost
Operating:management
Environmental
Infrastructure
Forcemajeure
Completion
Engineering
Political
Participant
Funding/interestrepayments
Syndication
Legal
Insurance ●
Ore Reserves ●
Engineering ●
Environmental ●
Tax ● ●
Accounting ●
6standardduediligencereports
expectedoneverydeal
Impact of 16 risks on due diligence reporting
50 is too many, 5 too few
12-20 risk categories is about the right balance

There are serious independence issues
• An insurance broker is not independent of companies
providing the insurances
• Tax and accounting/financial audit should be two separate
disciplines
• The people doing the technical review are often somehow
involved in the feasibility process or construction
• Environmental studies often draw heavily form a (cut and
paste) of nearest/adjoining area. That earlier scope of work
often colours the present one

The consulting game also has issues
• Some firms are too committed to their industry sector or are
beholden to that large sponsor. They cannot afford to be black-
balled. (for example they can’t tell BHP ‘they are wrong’)
• Some engineers know only their own sector and tend to be
‘one-eyed’ about anything else (SILO)
• Another variety of consultant is the ‘loss leader’. The initial
study (probably at a steep discount in study costs) is a marketing
precursor to continued work provided the project goes ahead.
• Some firms rest on their names and reputations as a way to earn
money for their Seal of Approval without doing as much as they
should

The consulting game also has issues
• Some consultants are for hire. Unfortunately they spell this
‘h-i-g-h-e-r’. The more they are paid, the more favourable
the report
• Some firms simply repackage earlier studies done for others
• Some consultants are too busy and cannot really focus on the
detail required
• Other consultants find anything outside their 25-30 year
career experience simply cannot be done
• Some consultants have their hobby horse opinions, methods
or equipment, which may not be the best option

Common procedural issues
• Mineral resource/reserve
– The most likely technical reason for project failure
• Mining rates
• Skipping steps (eg. PFS or SS)
• Over simplifying the level of complexity in modelling in the
early phases of project
• Doing things in the wrong order
– Doing experimental test work to validate the process flow sheet
– Doing EO last instead of closer to the beginning

This is what upsets investors the most
NPV makes the first few years critical to successful operation
Plant
design
capacity
Fast run
up to full
capacity
Slow run
up to
partial
capacity

At a mine site somewhere in West Australia…
200
250
300
350
400
450
500
550
600
40
45
50
55
60
65
70
75
80
85
90
MillPoerkW
MillFeedTonnes
Time (hours)
Grinding Circuit Performance
Hourly Average for 7 days
Mill Feed (tph) Mill Power (kW)
Process economics probably based on a steady state throughput

49
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
A*b =23 (hard ore) JKJKRBTRBTJKJKRBTRBT
Breakage Energy (kWh/t)
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
0
10
20
30
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50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
ErnestHenry
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Aqqaluk
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Boddington
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Bingham
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kwh/t)
SudburyPhase 1
Fitted Energy Breakage Curves
(T10 of RBT Product -11.2+9.5mm)
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
A*b Impact Breakage Parameter
(Ore hardness defines mill size and installed power)
Rock is variable…

50
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
ErnestHenry
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Aqqaluk
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Boddington
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Bingham
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
Rock is variable…

51
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
ErnestHenry
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Aqqaluk
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Boddington
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
Rock is variable…

52
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
ErnestHenry
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy (kWh/t)
Aqqaluk
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
Rock is variable…

53
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
ErnestHenry
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
Rock is variable…

54
T10 (%)
0.2 0.5 1.0 2.0
38.1
19.8
10.4
4.4
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12
T10
Energy(kWh/t)
CadiaEast
Prediction of
SAG mill behaviour
What
variability
would this
translate to in
the circuit?
Rock is variable…

“if Mungo wants to bang rocks together and call it
engineering, then there is nothing we can do about that…”
S. Walters (2009)
Also known as the FES
(Flat Earth Society)
Old school blues

To design one of these, disciplined and
sophisticated ore characterisation is required
Much more has to be put into initial ore body knowledge and risk
mitigation in design due to scale and complexity

Project Optimisation
Process/Infr
astructure
Engineering
Mine/Process
Operation
Schedule
Block model
generation
Mine
Engineering
Enterprise
Optimisation
Geometallurgy
Strategic
Options
Analysis

The geomet questions
• What mineralogy/lithology controls process behaviour
• Why?
• What controls that mineralogy?
• What are the process defined domains
• In spatial terms where are those domains in the deposit

Development of a geomet block model
• What process attributes need to be put in the block model?
– Comminution/recovery/penalty elements/etc.
• Are they additive?
• Should the process attributes go in or should the foundation
data go in so the attributes are calculated separately on
demand?
• The 3D variogram ellipsoid, nugget and range of inputs
• Does each block have a data value for each kind of process
attribute or is a multi-shelled block model appropriate
The block model is decisive for the effectiveness of EO

Where many current geomet programs fall over
Data Collection
• Samples collected without spatial coordinates in the ore body
• Tests done on parcel of rock in non-representative way
• Not enough samples collected
• Test work based on composites that mask variability
• Different tests done on wildly separate parcels of rock with very
few or no rock samples with more than one test type (for
example A*b and BMWi)
• The wrong hypothesis used to collect data
• No assay data collected with metallurgical testing
• Tests done years apart by different people and laboratories

Where many current geomet programs fall over
Analysis
• Test data not related to phenomenon being modelled
• Too many things being modelled at once, confusing the outcome
• Analysis done in isolation to the rest of mining process due to
mining culture limitations (SILO effect)

Geological Assays Petrophysical Equotip Comminution
Multi-disciplinary data collection
Geometallurgy interacting with engineering

Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A
Geometallurgical domains in block model

Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A
Process Attribute Ore Domain 1 Ore Domain 2 Ore Domain 3 Ore Domain 4
Ore Value
Valuable metal 1 (Au) grade 0.6g/t - - 1.1g/t
Valuable metal 2 (Cu) grade 1.20% - - 0.50%
Valuable metal 3 (Ag) grade - 2.2g/t 1.3g/t -
Valuable metal 3 (Mo) grade - 0.99% 0.47% -
Valuable metal x (?) grade
Penalty elements Yes (High As content) No No Yes (Low As content )
Ore Charatersiation
Mineral liberation size 75 micron 30 mciron 160 micron 212 micron
Property deportment siganture Yes (Upgrade factor 2.1) No No Yes (Upgrade factor 1.6)
Ore sorting feasible Yes No No No
Energetic conditioning feasible No No No No
Impact breakage energy consumption High High Very High medium
Low energy abrasion energy consumption High High High Low
Bed breakage energy consumption Medium Medium Medium High
Grinding Energy consumption High Medium High Low
Fine grinding energy consumption - Medium High -
Separation Process
Flotation recovery - High Medium -
Flotation kinetics - Medium Medium -
Leaching recovery Medium - - Low
Leaching kinetics Medium - - Low
Gravity Au recovery Yes - - No
Ore Domain 2 Ore Domain 4 Ore Domain 7

Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A
Ore Value
Ore Charatersiation
Separation Process
Ore Value
Ore Charatersiation
Separation Process
Pressure oxidisation required for Au recovery No - - Yes
Engineering Design Request
Floation circuit? - Yes Yes -
Concetrate regrind circuit? - Yes No -
Leach dump? No - - Yes
Leach heap? Yes - - No
Leach tank? No No No No
Ore Value
Ore Charatersiation
Separation Process

Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A
Ore Value
Ore Charatersiation
Separation Process
Ore Value
Ore Charatersiation
Separation Process
Ore Value
Ore Charatersiation
Separation Process
Separation Process
CiL carbon and leach circuit? Yes - - No
Ore sorting technology? Yes - - No
Energetic conditioning technology? No No No No
Crushing? Yes Yes Yes Yes
Grinding? No Yes Yes No
HPGR? No No Yes No
Fine grinding? No Yes No No
Ore Value
Ore Charatersiation
Separation Process

Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A
Ore Value
Ore Charatersiation
Separation Process
Ore Value
Ore Charatersiation
Separation Process
Ore Value
Ore Charatersiation
Separation Process
Separation Process
HPGR? No No Yes No
Ore Value
Ore Charatersiation
Separation Process
Separation Process
HPGR? No No Yes No
Ore Value
Ore Charatersiation
Separation Process

Process attribute to engineering
Axb
BMWi
Mineralogy
Recovery
Model
Grind Size
Recovery
Throughput Estimate
recoverable
Ni per hour
Estimate cost
of
productionPenalty
Elements

Use of the block model
• Those who build the block
model often don’t understand
how engineering works
• Those who might use the block
model often don’t understand
how it works
The block model
Engineering using data from
the block model
DynamicLink
(Traditionally
seenasstatic)
This is where many past efforts have not worked so well

Strategic management of
time and money

Definition of NPV
• Net Present Value (NPV) is the difference amount between
discounted sums: cash inflows and cash outflows
• It compares the present value of money today to the present
value of money in the future at a discounted rate to account for
inflation and returns
• A fundamental tool in Discounted Cash Flow analysis (DCF)
• NPV window is 5-8 years, after which cash flows are disregarded
NPV window = (1/5 CAPEX) + OPEX for 1 year
(rule of thumb)

Its all about the rock
Some ore blocks are:
• Higher grade than others
• Have harder ore than others
• Poorer recovery than others
• Have more penalty elements
So mining them in a different
schedule could bring in much more
revenue at the most effective time
in the NPV window.
BMWi-Predicted Values
Result: some blocks will
produce much higher
revenue than others.

What order we do things in makes all the
difference in the world
The consequences of getting this wrong is not just haemorrhaging
revenue but could be failure of business model for investor return

GeM Model
Economic Models
LS
LC
ROM
Leach
Crushers ProcessMine
LS
LC
ROM
Leach
Family of Solutions
Data Collection
Mining Sequences
Mine Plan
Feedback to improve model
Constraints Constraints
LS
LC
ROM
Leach
LS
LC
ROM
Leach
LS
LC
ROM
Leach
LS
LC
ROM
Leach
Slope Models
1 1.5 2 2.5 3 3.5 4
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
X
Probability Distribution of Expected Cash Flow @ PP-1
2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6
x 10
9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
F(x)
Empirical CDF
Cash flow stream
Capital
Investment
CurrentMine
ProjectValue
Production
Period
Millionof$
V
I
0t 1t 2t 3t 4t
()711tiiitWACCCFR==+
7t5t 6t
1 1.5 2 2.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
X
Probability Distribution of Expected Cash Flow @ Last PP
Simulation
Confidence Model
0 5 10 15 20 25 30
-1
0
1
2
3
4
5
6
7
x 10
8
Stochastic Expected Cash Flow
Production Period
CasfFlow
1 1.5 2 2.5 3 3.5 4
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
X
1.5 2 2.5 3 3.5 4 4.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
X
1 1.5 2 2.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
X
Probability Distribution of Expected Cash Flow @ Last PP
Probability of cash flow can be assessed for each year.
Project Optimisation – Intelligent Engineering
Data transfer technology now makes this
possible early in the design process
• All engineering sectors can now be optimised together
• The key is to do this in an iterative loop several times to ensure
overall operational efficiency

So what is the most
cost effective option?

Options Analysis & Theory of Constraints
Decision Tree for the Simple Capital Budgeting Example
Looking at bottlenecks in operations.
These will change over the life of a mine

• Truck concentrate or build a pipeline to the port
• For the LOM:
• Pipe is cheaper but is a bottle neck
• Trucking is more costly, may not be a bottle neck
• Pipeline and trucking is an option
• Trucks are very flexible (an optimisation value add) allowing cash
to be moved up-front, improving NPV
• Optimise whole project for 3 options: pipe, truck, pipe and truck
• Choose the option with the best NPV, progress outcome to next
study phase
Typical options and scenarios

Summary of the process of applying real
options when valuing a mine project
This process has the potential for a Green fields study to be useful in
a Brown fields corporate decision making context

Professional SILOS must continue to be
broken down
This works best when all stake holders concerned have no choice

Back to the word from London…
NPV
CAPEX
So what is the
real NPV?
(optimised EO)
What is the real needed
operation design?
(without CAPEX
blowouts)
As grade is decreasing this is not going to get easier any time soon.
INDUSTRY STANDARD PRACTICE WILL CHANGE

If you wish to dodge bullets, understand what you really are
looking at, or be good enough where you simply don’t have to

Project optimisation for
small and large studies
pit design, production plan & schedule
design criteria, flowsheet, mass balance etc
block model
geomet data (mill, float, leach, geomechanical test data)
option and scenario
analysis
AMDAD/Ausenco LiteO Whittle/Ausenco EO
PFS
FS
best possible
optimised
outcome
Ausenco now has access to this expertise

Ausenco Project Optimisation
• When industry is in a growth cycle
• How can we help your business make more money
most efficiently
• When industry is in a contraction cycle
• How can we help your business to survive in a
challenging environment
• Flexible decisions can now be made in a
defendable form, fit for purpose to the macro
business environment
Engineering expertise merges
with corporate decision making

The platform is burning – the industry doesn’t see it yet
and is trying to enforce ‘business as usual’
• The investment community are now demanding more
credible FS plans that deliver what they promise
• Conventional methods of estimation simply aren’t good
enough any more for the more marginal deposits
• Decreasing grade (among other things) demonstrate that this
difficult environment is only going to get worse
• New technology and integrated systems of analysis have the
potential restore investor confidence
• Industry will be forced to change its standard practice
A business opportunity for those at the right place at the right time

The problem
is not the problem.
The problem is your
attitude to the problem.
Do you understand?
Captain Jack Sparrow
(some time ago)

How New Due Diligence is Transforming Mining Operations

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