Simulation and Risk Analysis

8/13/2019 Simulation and Risk Analysis

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Risk assessment using Minesight

software & Python scripting


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Presentation aim

To highlight the risks present in resource and

reserve estimation and give an insight into some

of the tools available in Minesight that can be used

to determine grade risk.


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Presentation Summary

Snowden overview

Risk factors in mining

Simulation for grade risk

Confidence limits and probability above cutoff

Case Study: Grade riskpython scripting.

Handy hints Questions


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Snowden overview

Downer EDI

Snowden (160+ people worldwide)

Resource Evaluation Group

Mining Engineering Group

Geotechnical Engineering Group

Corporate Services Group (Audits, Valuations) Business Improvement Group (Six Sigma)

Risk Management Group

Mentoring and Training

Technologies (Supervisor, Reconcilor)

Offices in Perth, Brisbane, Johannesburg, Vancouver and London.

For further details refer to www.snowdengroup.com


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Minesight experience / training

12+ years experience using Minesight in grade control, geological

modelling, stockpile modelling, resource estimation, resource

classification, statistical and spatial analysis, mine planning,

scheduling and pit optimisation

Snowden also deliver in depth training courses and detailed training

manuals are also available:

Snowden resource estimation guide using Minesight

software*

Statistical and spatial analysis using MSDA* Kriging and block model validation

Resource estimation and classification

Simulation and risk analysis


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Involvement in Minesight Software

Development

Boddington Gold Mine, Western Australia Inclined benches

Lihir Gold Mine, Papua New Guinea

Stockpile Modelling

Mt Isa Copper Mine and George Fisher Mine, Queensland Data Security System

Multirun tool

Drillhole design tool

Compositing weighting

Kriging engine

Easting offset (unfolding)

Geomap tool

Minesight Data Analyst (MSDA)

Block modelling & resource evaluation


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Risk?

Risk = Likelihood x Consequence

What is the likelihood that you will be injured?

How severe will be your injuries?

Is the risk acceptable?

If you understand the risks present then youcan mitigate the impact of these risks withgood management and decisions.

Poor Risk Management!


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Cultural

FeaturesHumanNatureRisk

Economic

Uncertainty

Dynamic

Constantly

Changing

Topography

Mineral Types

Mineralisation Limits

Lithology

Geotechnics

COMMODITY PRICES

LABOUR

COSTSINFLATIONINTEREST

RATES

PROCESS

CAPCOSTS

Risk factors in mining

MotherNatu

reRisk

GeologicalUncertainty

More difficult

to quantify


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Resource and Reserve risk

RISKis commonly not

quantifiedat any of the

technical stages

UNCERTAINTYinherent in each stage

Resource and Reserve estimates

Oredefinition

Geologicalinterpretation

Resourceestimate

Reserveestimate

Mineplanning

The greater theuncertainty thegreater the risk!

**Uncertainty associated with geological interpretation and grade estimation is usuallythe largest source of potential error in the resource and reserve estimate**


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What is grade risk?

Grade Risk: The risk of not meeting estimated grade

Grade risk is a function of the grade variability present within theselected mining unit (panel) and the probability that the grade

present within that panel exceeds the economic cutoff grade

High risk blocks would have a high probability that the grade minedfrom that block would be less than the economic cutoff grade

The greater the grade variability present

The greater the risk thatthe estimated grade is not achieved

Low risk blocks would be in areas of consistent grade and theprobability that the estimated grade of the block exceeding theeconomic cutoff grade would be high


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How to determine grade risk?

Simulation is the answer

Conditional Simulation gives the user numerous equi probable results for any panel. (Aminimum of 100 realisations is recommended)

Simulation is typically completed external to Minesight due to the current limitation of items in

the block model

Reality

Multiple realisationsConditional Simulation


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Confidence limits and probability

above cutoffs

0

1

2

3

4

5

6

7

8

9

1 2 5 10 15 19 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99Mean Ranked Simulations

SiO2Grade(%)


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Distribution of simulated grades

The greater the spread ofsimulated grades for a panelThe greater the risk

Low confidence

large variance wide spread

large range of potential values

Potentially high risk region.

High confidence low variance narrow spread

small range of potential values

Potentially low grade risk regionCumulativeFrequency

Spread of grades0

1

Cu

mulativeFrequency

Spread of grades0

1


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Example of calculation of error by

confidence limit (c.l.)

95thpercentile

5thpercentile

c.l.90%aterror2

5th95th

Absolute error

Absolute error for a given 10m x 10m panel

is: 4.29% SiO2 1.73% S at 90% c.l.

Relative error for the same 10m x 10m

block is: 4.29% SiO2 40.0% at 90% c.l.

5.90 - 2.45 = 1.73

2

Simulation

Mean Rank SiO2 Grade (%)

1 1.75

5 2.45

10 2.71

25 3.53

50 4.13

75 5.10

90 5.60

95 5.90

100 7.70

Mean 4.29

Relative error

1.73 = 40%

4.29

Absolute Error x 100

Mean


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Simulation grade range map

(90% Confidence)

Blue regions show low grade variation and are potentially low risk areas Yellow regions show high grade variation and are potentially high risk areas


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Probability above cutoff maps

Probability map ofSiO2 grade beingabove 4.0%

High probability /high risk areas arered

Probability map ofSiO2grade beingabove 6.0%

High probability /High risk areas arered

Probability Calculation 90% c.l.

(82 / 90) x 100 91%

90

x 100

Number of

realisations above

cutoff grade within

90% CI


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Panel error investigation

Average relative error at different panel sizes and at

different confidence limits

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Com

posites

10m

x10m

20m

x20m

30m

x30m

40m

x40m

50m

x50m

100m

x100

m

200m

x200

m

300m

x300

m

400m

x400

m

500m

x500

m

Panel Size

A

verageRelative

Erro

r(%)

90% Confidence 80% Confidence 50% Confidence


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Analysis of first three years production

01

2

3

4

5

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

2,500,000 3,500,000 4,500,000 5,500,000 6,500,000 7,500,000 8,500,000

Tonnage

Grade

Maximum grade simulation

Minimum grade simulation

Median grade simulation

Range in tonnage at a given grade

Range in grade for a fixed tonnage

Probability and risk analysis


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Schedule risk

Grade Variation by Scheduled Year

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

0 2 4 6 8 10

Year

Grade

Sim maximum

Sim minimumSim median

Kriged estimate


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Case Study:

Risk calculation - python scripting

Python scripts areeasily developedby Mintecpersonnel and savesignificant time andeffort

Python scripts arecommonly storedunderc:\medexe\site\scripts


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Load simulations into Minesight

Load mean ranked simulations to the block model. Due to the limitation of the number

of items in the block model it is not possible to load all the simulations to a single

block model. Only load the minimum, 5th, 10th, 25th, 50th, 75th, 90th, 95thand maximum

ranked simulation values to the block model

In addition it is good practice to also load the mean, variance, standard deviation and

coefficient of variation of the simulations to the block model


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Load probability above specified

cutoffs into Minesight

Load probability values above specific cutoffs into the Minesight block model. The

example above shows probability values for SiO2exceeding 2.0%, 4.0%, 6.0%, 8.0%,

10.0%, 12.0% and 14.0% being loaded to block model items SIP02, SIP04, SIP06,

SIP08, SIP10, SIP12 and SIP14 respectively


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Calculate grade range at different

confidence limits for each panel

Using block modelUser Calcs the grade range between the 50% confidence limit(25thto 75thranked simulation), 80% confidence limit (10thto 90thranked simulation),90% confidence limit (5thto 95thranked simulation) and all simulations werecalculated for each panel and stored in the block model items S2575, S1090, S0595and S1100 respectively


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Calculate the relative error at different

confidence limits

For each panel the relative error for each of the confidence limits iscalculated and stored to the block model. The relative error iscalculated by dividing the simulation grade range for each confidencelimit by the simulation mean and multiplying by 100


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Calculation of grade risk


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Simulation grade risk matrix

1.00

0.95 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

0.90 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

0.85 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

0.80 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

0.75 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0.70 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

0.65 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

0.60 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

0.55 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

0.50 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.45 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0.40 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

0.35 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

0.30 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

0.25 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0.20 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.15 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0.10 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

0.05 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Very Low Risk (1 to 5)

% Grade Variance from Mean (Risk 95 = Grade Variance of PCU05 to PCU95). Other Risk Options - Risk 90, Risk 75.

Probability

theBlockGradeisbelow

theRes

ourceCutoffGrade(90%

c.l.)

Moderate Risk (9 to 10) Very High Risk (16 to 20)Extreme Risk (>20)

Low Risk (5 to 8)

High Risk (11 to 15)


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Risk maps

Risk maps are a powerful design tool for engineers and geologists


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Handy hints

Use MSDA custom reports to report risk for multiple domains at

multiple confidence limits

Use MSDA custom reports to complete statistical comparisons of

simulation data against the original composite data

Import simulation and composite variograms into Minesight andcompare visually using ImportVariograms (ASCII) file

Block model statistical summaries by northing and easting can be

completed easily in MSDA via custom reports. Setup a filter tab

based on easting or northing and input bins based on appropriate

spacing. Use MSDART to manage large ASCII and CSV files.


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Minesight grade risk analysis

procedure summary

1. Define area(s) of interest.a. Area of interest should be larger than range of variogram

2. Extract data within domainsa. Code drillholes and block model to geological, structural, weathering and density domains

3. Statistics and variography per domain for each element.a. Complete statistical analysis of raw and declustered drillhole data using MSDA custom reports

b. Complete statistical analysis of composite data using MSDA custom reportsc. Complete statistical analysis of composite by easting, northing and RL using MSDA custom reports

4. Kriging Search Optimisation.a. Use kriging debug tool to evaluate kriging weights

b. Summarise regression slope values, simple kriging weights and kriging variance via MSDA customreports

5. Conditional Simulationa. Run sequential Gaussian simulation (Minimum of 100 simulations per node recommended)b. Select a node spacing which divides into panel / standard mining unit (SMU) evenly. (A minimum of

25 nodes per panel is recommended)

6. Simulation Validationa. Visual Checks

b. Statistical checks using MSDA.

c. QQ-plots

d. Simulation variogram checks against composite variogram model


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Minesight grade risk analysis

procedure summary

7. Reblock simulations to appropriate panel size or standard

mining unit (SMU).

8. Sort simulations per panel by grade and calculate grade range

and probabilities above cutoff at selected confidence limits.

9. Calculate grade risk using python scriptsa. Ensure simulation risk matrix is correct and within Minesight project

b. Complete statistical analysis of risk by domain and by northing, easting and RL using MSDA

10. Develop risk maps

11. Calculate risk per mining period, stope or region and evaluate

mine plan with respect to risk.


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Questions

Bring on the bulls!

Pamplona here I come!

Risk Management? Snowden can help.

For further details refer to www snowdengroup com

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Simulation and Risk Analysis

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