Why Mines Failblog.rpacan.com/wp-content/uploads/2017/03/Why-Mines-Fail.pdf · Why Mines Fail March 6, ... • Advice to the mining industry for 30 years ... Internal Dilution. Dilution

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Why Mines Fail

March 6, 2017

Seeking Better Mining Outcomes in the Next Cycle

Presented by: Richard J. Lambert, MBA, P.E., P.Eng.


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About RPA

• Advice to the mining industry for 30 years • The specialty firm of choice for resource and reserve work • All stages:

• exploration and resource evaluation • scoping, prefeasibility and feasibility studies • financing, permitting, construction, operation, closure and rehabilitation.

• Clients are financial institutions, governments, major mining companies, exploration and development firms, law firms, individual investors, and private equity ventures

• Offices in Canada, United States and United Kingdom • Head office in Toronto • 100% employee owned


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Outline

• Why Do Mines Fail? • Common Threads • Fatal Flaws • Non-Fatal Flaws that can be overcome


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Why do Mines Fail?

• Poor Management • Built to be more than it really was • Mineral Resource not there • Prices (up or down) • Level of Study (moving too fast) • Insufficient funds


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Failed/Troubled Projects Since 1985

• RPA review identified 75 projects that failed to meet expectations in a material way.

35 Grade Related

10 Mining Method

16 Processing

8 Capital Cost

7 Operating Cost

6 Social/Permitting/Government

5 Prices/Timing


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Common Threads

• Head Grade - Resource Reserve models. • Basic errors or incorrect assumptions in resource estimation.

• Productivity and misapplication of mining method • Mining extraction overestimated and mining dilution underestimated.

• Processing, including metallurgy, equipment selection, and ramp up.

• Capital costs underestimated.

• Operating costs underestimated.

• People and Technology. • Lack of experience / Team size • Substituting software for judgment.

• External Pressures.

• Time and cost constraints.


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The results?


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Resource Head Grade

• Geology – understand the deposit, do not let the software drive the interpretation and replace judgement

• Collect enough data

• Database integrity – QA/QC.

• Capping.

• Specific Gravity or Bulk Density?

• Cut-off grades – break even or incremental? • Checkerboarding

• Low grade deposits and accuracy of estimation.

• Reconciliation.

• Grade smearing


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LEGEND: Grey = Blocks Below Cut-Off Red = Blocks Above Cut-Off

The Effect of Cut-Off Grade = 1.0 g/t


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3.0 g/t


11


5.0 g/t


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Dilution

• Mining method and production rate.

• Minimum width.

• Ground conditions.

• Equipment selection.

• Mining skills.

• Technical and supervisory skills.

• Grade Control and sampling.

• Assay turnaround.

• Most underestimated of all technical parameters.


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MineralizationOutline

0.8

3.43.7

0.5

3.5

0.24.4

0.50.5

4.52.5

2.63.6

1.5

0 .8

3 .40 .7

0.5

3.5

3. 4

0.3

0.5

0. 5

0.5

0.7

0.8

1 .1

1.5

InternalDilution

Dilution & Extraction


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PlannedDilution

PlannedDilution

Drift

Drift

DesignedStope


PlannedLoss

PlannedLoss



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Drift

Drift

DesignedStope


UnplannedDilution

UnplannedDilution

Actual Stope



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Minimum Mining Width

• Vein width vs. equipment requirements – dilution.

• Block size for resource model. Narrow vein with big blocks or bulk mining with small blocks.


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Minimum Mining Width


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5 x 5 m 2.5 x 2.5 m Considerations include: - Ability to locate individual blocks in BH pattern - Excavate entire block cleanly – loading unit - From a shot-rock pile that has moved

Selective Unit – Open Pit Mine


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Head Grades - Actual Production vs PFS and FS Studies

Based on 70 Projects


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Productivity

• Not enough workplaces = less flexibility in production plan = higher dilution.

• Mine operators will generally always deliver the required tonnes, but sometimes there is no ore in them.

• Zero base – use cycle times, not factors.

• Lay out entire mine at the start, including all development.

• Schedule in detail – monthly, quarterly, annually, life of mine.

• Detailed 3 month plan for operations.

• Underground – 50 metres vertical per year.

• Open pit – 8 to 12 benches per year.


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Processing

• Metallurgical assumptions.

• Enough testwork to establish recoveries, con grades and impurities.

• Bench scale, Locked cycle tests, Grinding tests, Pilot Plant.

• Bulk Sample.

• Non Standard or new technology.

• Plant ramp up.

• Can range from two weeks to never.

• Should forecast four to six months at the FS level, for standard technology.

• Allow for experienced startup team and many people.

• Ensure assay lab is running and can turnaround samples quickly.


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Capital Costs

• RPA database from 2002.

• EMJ Gypton Jan 2002 and recent work by RCF from 2005.

• RCF also gathered historical data from 1965.

• Mining Engineering Bullock Apr 2011.

• Close to 200 projects.

• General average overrun 20% to 25%.

• Much higher in times of high metal demand and prices.

• Not just mining – analysis of other projects pre-2002 showed an average overrun of 28% - going back 90 years.


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Capital Cost Overruns - Experience

-50%

0%

50%

100%

150%

200%

250%

300%

$10 $100 $1,000

Cos

t Ove

rrun

Original Project Estimate ($M)

Capital Cost Overruns in Mine Construction


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Capital Cost Overruns – By Project

-20%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Actu

al C

apita

l Cos

t C

ompa

red

to B

udge

t


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Costs Example – How Reliable are Study Estimates

• 73% Increase of Capital Costs from Prefeasibility Study (PFS) to Updated Feasibility Study (UFS).

• Operating Cost increase not as dramatic, but still 37% higher than PFS.

• Source of cost escalation largely in labour, consumables, equipment.

• This is not atypical for recent mining projects, across all commodities, mine types, locations, and company sizes.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

$-

$5

$10

$15

$20

$25

Oct-09 PFS Jun-10 FS Oct-12 UFS Feb-14 ACT

Hea

d G

rade

(gpt

)

OPE

X ($

/t pr

oces

sed)

Operating Costs and Grade - OP Gold in Canada

OpEx ($/t processed) Head Grade (gpt)

$-

$0.5

$1.0

$1.5

$2.0

$2.5

$3.0

Oct-09 PFS Jun-10 FS Oct-12 UFS Oct-13 ACT

Cap

ital C

osts

Bill

ions

Large OP Gold in Canada

INIT CAP SUST CAP


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Changes in Study Estimates

$-

$50

$100

$150

$200

$250

$300

$350

$400

$450

$500

Oct-06PEA

Sep-07PFS

May-08FS

Sep-09UFS

Aug-10ACT

Initi

al C

apita

l Cos

t (M

illio

ns)

Capital Cost Escalation - Mid Size OP Gold in W. Africa

$-

$50

$100

$150

$200

$250

Feb-06 PEA Aug-07 FS Dec-07 UFS Mar-09 ACT

Initi

al C

apita

l Cos

ts (M

illio

ns)

Mid Size OP & UG Gold/Silver in Mexico

15% increase in CAPEX from PEA to Actual

52% increase in CAPEX from PEA to Actual


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Operating Costs

• Larger is lower unit cost.

• Smaller is higher unit cost.

• Complexity is higher cost.

• Remote areas are higher cost.

• The key cost drivers will change from area to area, mine to mine and process to process. What was key for a remote Canadian operation may not be key to a remote operation in another country.


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Where Do Operating Cost Estimates Go Wrong?

• Productivity assumptions in every separate unit operation

• Tonnes per manhour

• Tonnes per hour of equipment

• Metres of development per day

• Productivity less at high elevations

• Understanding fixed vs variable costs.

• Missing the impact of administration on a unit cost basis especially in low tonnage operations (administration is fixed not variable).

• Believing that a zero base budget (or cost estimate from first principles) is correct just because there is a huge amount of detail in the calculations.


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Where Do Operating Cost Estimates Go Wrong?

• Omitting items (administration, benefits, fees, head office, warehousing, support functions).

• Factoring based on other operations with similar methods.

• Not including costs for “high end” products that may be needed – winter diesel, biodiesel, galvanized bolts, stainless steel products.

• Over complicated spreadsheets resulting in logic and arithmetic errors that defy auditing

• Failure to keep track of edits and changes to costs including the rationale for such changes.

• Believing that a fixed price contract passes the risk to the contractor or supplier.


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Fixed Price Contracts and Cost Control


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People and Technology

• Knowledge and experience gap.

• Lack of skilled people at all levels.

• Majors have abandoned the traditional role of training engineers and managers.

• Reliance on software in replacement of judgment = precision over accuracy.

• Statistical analysis is overrated. Should I really feel more comfortable if someone tells me the project is P90?

• Process related analysis can lead to a false sense of security, e.g. risk assessments.

• Team Size Matters - Project complexity is the primary driver

• Teams are commonly too small (especially for mega projects).

• Very common to see vacant positions on the org chart.


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Non-Fatal Areas That Can Be Overcome

• Schedule Delay • Ramp-Up Delay

Discussed Previously • Capital Cost Overruns • Management and People • Level of Study

Individually these problem areas can usually be overcome, when combined, may again be fatal.


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Schedule Delay

• The primary purpose of the schedule developed by the EPCM firm is to provide a target for project completion.

• The Master Schedule typically excludes any contingency.


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Ramp-Up

From: Terry McNulty

Mining Engineering Oct 1998


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More Observations

1. Precision vs Accuracy - believing that detail makes it all correct. 2. Believing that cheap labour means lower cost . 3. Not benchmarking. 4. Truisms that aren’t.

• We can do better than everyone else – we have a better team.

• If that’s all the money we can raise, then that’s what we will build it for.

• The contractors should carry the risk – or - don’t worry, we have fixed price contracts.

• The EPCM people have promised us their best team.


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Summary

• Objective: • Provide background on why mining projects fail • Describe fatal and non-fatal failures • Describe problem areas to understand impact

• Outcome • Understanding risks for mining projects • Know when to get out


Toronto Denver London Vancouver Quebec City

Richard J. Lambert, P.E. Tel: +1 (303) 800-8104 Email: [email protected]

mailto:[email protected]

Documents

Why Mines Failblog.rpacan.com/wp-content/uploads/2017/03/Why-Mines-Fail.pdf · Why Mines Fail March 6, ... • Advice to the mining industry for 30 years ... Internal Dilution. Dilution