the bankers are turning nasty v3

"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

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Cas

h C

ost

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Gold Producers -(what they when raising captial to fund venture)

Reported cash costs ($/oz)

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Source: Bell Potter 2011

An average blowout of 54% of original cost of production estimate

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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

ProjectCompany

Feasibility

budget cost

Actual/forecast

cost overrun

Ravensthorpe/Yabilu

ExpansionBHP 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?

Multifactor Productivity

The big squeeze and technology solutions Technology – extraction

Andrew Mackenzie, Group Executive and Chief Executive Non-Ferrous Slide 25

0.0%

0.5%

1.0%

1.5%

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2.5%

3.0%

3.5%

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4.5%

0

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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 (1984 onwards).

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, 0.7Laboratory/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

Supp

ly/i

nput

s/or

e re

serv

e

Mar

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Fore

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exch

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Ope

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echn

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ost

Ope

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anag

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Envi

ronm

enta

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Infr

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Forc

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ajeu

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Com

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Engi

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Polit

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Part

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Fund

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inte

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repa

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Synd

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Lega

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Insurance ●

Ore Reserves ●

Engineering ●

Environmental ●

Tax ● ●

Accounting ●

6 s

tan

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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…

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Grinding Circuit PerformanceHourly Average for 7 days

Mill Feed (tph) Mill Power (kW)

Process economics probably based on a steady state throughput

49

T10 (%)

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A*b =23 (hard ore) JKJKRBTRBTJKJKRBTRBT

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Aqqaluk

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Sudbury Phase 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…

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T10 (%)

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Prediction of

SAG mill behaviour

What

variability

would this

translate to in

the circuit?



Rock is variable…

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T10 (%)

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Prediction of

SAG mill behaviour

What

variability

would this

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the circuit?



Rock is variable…

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Aqqaluk



Prediction of

SAG mill behaviour

What

variability

would this

translate to in

the circuit?



Rock is variable…

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T10 (%)

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Ernest Henry



Prediction of

SAG mill behaviour

What

variability

would this

translate to in

the circuit?



Rock is variable…

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T10 (%)

0.2 0.5 1.0 2.0

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Cadia East



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/Infrastructure

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

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

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 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


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No





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


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No





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


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Process attribute to engineering

Axb

BMWi

Mineralogy

Recovery Model

Grind Size

Recovery

Throughput Estimate recoverable Ni per hour

Estimate cost of

production Penalty 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

Dyn

amic

Lin

k

(Tra

dit

ion

ally

se

en a

s st

atic

)

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 108

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 109

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

Cap

it al

Inv e

stm

e nt

Cur

rent

Min

eP r

ojec

tV

alu e

Production Period

Milli

onof

$

V

I

0t 1t 2t 3t 4t

()711tiiitWACCCFR==+

7t5t 6t

1 1.5 2 2.5

x 108

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

7x 10

8 Stochastic Expected Cash Flow

Production Period

Casf

Flo

w

1 1.5 2 2.5 3 3.5 4

x 108

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 108

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

X


1 1.5 2 2.5

x 108

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)

Questions?

the bankers are turning nasty v3

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