Risk assessment using Minesight software & Python scripting
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.
Presentation Summary
• • • • • • •
Snowden overview Risk factors in mining Simulation for grade risk Confidence limits and probability above cutoff Case Study: Grade risk – python scripting. Handy hints Questions
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
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
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
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 you can mitigate the impact of these risks with good management and decisions.
Poor Risk Management!
Mother Nature Risk Human Nature Risk
Risk factors in mining INTEREST RATES
Economic Uncertainty Dynamic Constantly Changing
PROCESS
INFLATION
LABOUR COSTS
COMMODITY PRICES
CAPCOSTS
Cultural Features
Topography
Geotechnics
Lithology
Geological Uncertainty More difficult to quantify
Mineral Types Mineralisation Limits
Resource and Reserve risk Resource and Reserve estimates Ore Geological Resource definition interpretation estimate
Reserve estimate
Mine planning
UNCERTAINTY inherent in each stage
RISK is commonly not quantified at any of the technical stages
The greater the uncertainty the greater the risk!
**Uncertainty associated with geological interpretation and grade estimation is usually the largest source of potential error in the resource and reserve estimate**
What is grade risk? Grade Risk: The risk of not meeting estimated grade • Grade risk is a function of the grade variability present within the selected 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 mined from that block would be less than the economic cutoff grade • The greater the grade variability present The greater the risk that the estimated grade is not achieved • Low risk blocks would be in areas of consistent grade and the probability that the estimated grade of the block exceeding the economic cutoff grade would be high
How to determine grade risk? Simulation is the answer Conditional Simulation
Multiple realisations
Reality
• •
Conditional Simulation gives the user numerous equi probable results for any panel. (A minimum of 100 realisations is recommended) Simulation is typically completed external to Minesight due to the current limitation of items in the block model
Confidence limits and probability above cutoffs 9
8
7
SiO2 Grade (%)
6
5
4
3
2
1
0 1
2
5
10
15
19
20
25
30
35
40
45
50
55
60
Mean Ranked Simulations
65
70
75
80
85
90
95
99
Distribution of simulated grades • The greater the spread of simulated grades for a panel The greater the risk
Cumulative Frequency
1
• Low confidence 0
Spread of grades
• • • •
large variance wide spread large range of potential values Potentially high risk region.
Cumulative Frequency
1
• High confidence • • • • 0
Spread of grades
low variance narrow spread small range of potential values Potentially low grade risk region
Example of calculation of error by confidence limit (c.l.) Simulation Mean Rank 1 5 10 25 50 75 90 95 100 Mean
Absolute error SiO2 Grade (%) 1.75 2.45 5th percentile 2.71 3.53 4.13 5.10 5.60 5.90 95th percentile 7.70 4.29
95th 5th error at 90% c.l. 2 5.90 - 2.45 = 2
1.73
Absolute error for a given 10m x 10m panel is: 4.29% SiO2 1.73% S at 90% c.l.
Relative error Absolute Error Mean
1.73 4.29
=
x
100
40%
Relative error for the same 10m x 10m block is: 4.29% SiO2 40.0% at 90% c.l.
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
Probability above cutoff maps • •
Probability map of SiO2 grade being above 4.0% High probability / high risk areas are red
Probability Calculation 90% c.l. Number of realisations above cutoff grade within 90% CI x 100 90
(82 / 90) x 100 91%
• •
Probability map of SiO2 grade being above 6.0% High probability / High risk areas are red
Panel error investigation Average relative error at different panel sizes and at different confidence limits
70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00%
Panel Size 90% Confidence
80% Confidence
50 0m
50 0m
x
40 0m
40 0m
x
30 0m
30 0m
x
20 0m
20 0m
x
x
10 0m
50 m 10 0m
50 m
x
40 m
40 m
x
30 m
30 m
x
20 m x
20 m
10 m
x
10 m
ite s
0.00%
Co m po s
Average Relative Error (%)
80.00%
50% Confidence
Probability and risk analysis Analysis of first three years production 12.00
11.00
Maximum grade simulation
10.00
Grade
9.00
8.00
Minimum grade simulation 5
Median grade simulation 4
Range in tonnage at a given grade 7.00
3
Range in grade for a fixed tonnage 6.00
2
5.00
4.00 2,500,000
1
3,500,000
4,500,000
5,500,000 Tonnage
6,500,000
7,500,000
0
8,500,000
Schedule risk Grade Variation by Scheduled Year
12.0 11.0 10.0
Grade
9.0 8.0 7.0 6.0
Sim maximum
5.0
Sim minimum
4.0
Sim median Kriged estimate
3.0 2.0
0
2
4
6 Year
8
10
Case Study: Risk calculation - python scripting
•
Python scripts are easily developed by Mintec personnel and save significant time and effort
•
Python scripts are commonly stored under c:\medexe\site\ scripts
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, 95th and 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
Load probability above specified cutoff’s into Minesight
•
Load probability values above specific cutoffs into the Minesight block model. The example above shows probability values for SiO2 exceeding 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
Calculate grade range at different confidence limits for each panel
•
Using block model ‘User Calcs’ the grade range between the 50% confidence limit (25th to 75th ranked simulation), 80% confidence limit (10th to 90th ranked simulation), 90% confidence limit (5th to 95th ranked simulation) and all simulations were calculated for each panel and stored in the block model items S2575, S1090, S0595 and S1100 respectively
Calculate the relative error at different confidence limits
•
For each panel the relative error for each of the confidence limits is calculated and stored to the block model. The relative error is calculated by dividing the simulation grade range for each confidence limit by the simulation mean and multiplying by 100
Calculation of grade risk
Simulation grade risk matrix Probability the Block Grade is below the Resource Cutoff Grade (90% c.l.)
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 10
0 20
0 30
0 40
0 50
0 60
0 70
0 80
90
100
110
120
130
140
150
160
170
180
190
% Grade Variance from Mean (Risk 95 = Grade Variance of PCU05 to PCU95). Other Risk Options - Risk 90, Risk 75.
Very Low Risk (1 to 5) Low Risk (5 to 8)
Moderate Risk (9 to 10) High Risk (11 to 15)
Very High Risk (16 to 20) Extreme Risk (>20)
200
Risk maps
•
Risk maps are a powerful design tool for engineers and geologists
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 and compare visually using Import Variograms (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.
Minesight grade risk analysis
procedure summary 1.
Define area(s) of interest. a.
2.
Extract data within domains a.
3.
Use kriging debug tool to evaluate kriging weights Summarise regression slope values, simple kriging weights and kriging variance via MSDA custom reports
Conditional Simulation a. b.
6.
Complete statistical analysis of raw and declustered drillhole data using MSDA custom reports Complete statistical analysis of composite data using MSDA custom reports Complete statistical analysis of composite by easting, northing and RL using MSDA custom reports
Kriging Search Optimisation. a. b.
5.
Code drillholes and block model to geological, structural, weathering and density domains
Statistics and variography per domain for each element. a. b. c.
4.
Area of interest should be larger than range of variogram
Run sequential Gaussian simulation (Minimum of 100 simulations per node recommended) Select a node spacing which divides into panel / standard mining unit (SMU) evenly. (A minimum of 25 nodes per panel is recommended)
Simulation Validation a. b. c. d.
Visual Checks Statistical checks using MSDA. QQ-plots Simulation variogram checks against composite variogram model
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 scripts a. b.
Ensure simulation risk matrix is correct and within Minesight project 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.
Questions
Bring on the bulls! Pamplona here I come!
Risk Management? – Snowden can help. For further details refer to www.snowdengroup.com
