Embed Size (px)
Citation preview
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4 SIMULIA/GEOVIA Solutions for Tactical Mine Planning
By Craig Bradley
2
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
The Challenge
“In an ever changing world, where velocity of information flow can haveimmediate effect on the economics of mining concerns, the lack of managementdecision tools may be disastrous for both the mining company and itsstakeholders. In the open pit mining environment, there has been substantialdevelopment of optimization tools. However, this has been lacking in theunderground mining environment.”
- Ballington* 2015
*A practical Application of an economic optimization in a underground environment. I Ballington 2015 (Emphasis added)
3
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
A New Solution for Mine Planning Strategic Mine Planning, a process where
mine planning is integrated and aligned with the strategic objectives of the company, which involves continuous adjustments to changes in the business environment
A common aim is maximizing the value realized from extracting a mineral resource by varying the input parameters in a flexible mine planning system for a desired balance between financial and physical returns
4
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Isight vs. Manual Optimization
Time (hours)
Mine
Plan
Qua
lity
Target Quality Level
Manual Optimization
10 20 30 40 50 60 70 1301201101009080 170160150140
Shorter time in study and improved NPV results
5
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Deterministic Scheduling using MineSched Single path solution for geometry, sequence and equipment schedule
Stope Geometry Development Sequences Schedule
6
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Challenging the Status Quo Status Quo In the real world, scheduling is never a single path solution.Many input parameter variations with the potential to create thousands of “What if” scenariosExcel to analyze combination of input parameters and output responses works both ways
and finding the balance is always expensive, tricky and challenging.
Proposed SolutionSimpler and efficient by Isight processing many scenarios Improved decision making by including analysis tools and statistics
7
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
SIMULIA Isight Workflow
8
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Design of Experiments (DOE)
Y1
ConstraintBoundary
Y2
Initial Best Plan
Feasible Non-feasible(safe) (failed)
X2
X1
Outputs
Inputs
DOE:Design Space
9
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Optimization
Y1
ConstraintBoundary
Y2
Initial Planfrom DOE
Feasible Non-feasible (safe) (failed)
OutputsImprove Plan Performance
Optimization
Optimized Plan
Optimization & Planning Exploration
10
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Manage Risk
Y1
ConstraintBoundary
Y2
Feasible Non-feasible (safe) (failed)
OutputsImprove Plan
Quality
Robustness and Reliability Analysis and
Optimization
Robust and ReliablePlan
% Unreliable% Reliable
11
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
GEOVIA MineSched Parameter driven Easy to use auto-scheduler Intuitive Graphical interface Configured to be used with a
wide range of mining methods
12
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Design Objectives - Optimizing Equipment Rates Include a range of rates where the mine planner can measure response to NPV Provide industry standard outputs Provide robust schedule by measuring sensitivities by simulation
13
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Process FlowDeterministic Scheduling to Maximize NPV
Construct Financial Model
Identify Input & Output Parameters in
MineSched
Configure Isight Workflow with
MineSched Scenario
Create Attributes for Input & Output
Variables
Map Reports Generated from MineSched using
Excel component of Isight
Set Objectives and Execute
GEOVIA MineSched SIMULIA Isight
Execution continues till objectives are met
14
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Typical financial model in MineSched: developed by defining Exchange rates, refining charges, ore values, process cost, mining cost etc.
Financial Model
15
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Inputs: Jumbo rate - developmentBogger rate - production
Input and Output Variables from GEOVIA MineSched
Outputs:Mining - Cost at time of miningProcessing - Revenue at time of process
16
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
DOE Inputs for Scheduling
17
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Exploring using DOE
-0.03
Run # = 4 Bogger Rate = 3975 t/dJumbo Rate = 32.1 m/d
=0.88
18
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Optimum design point:
Bogger Rate = 3997.87 t/dJumbo Rate = 33.22 m/dNPV = mil$129
19
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Case Study
=0.92
0.59
20
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Uncertainty Analysis100 Random VariablesBogger Rate
Mean 3997
Standard Deviation 399.78
Coeff. of Variation 0.1
Jumbo Rate
Mean 33.22
Standard Deviation 3.32
Coeff. of Variation 0.1
Process Rate
Mean 4790.0
Standard Deviation 479.0
Coeff. of Variation 0.1
ResponseNPVMean 124,653,826 Standard Deviation 9548708 Minimum 104,297,464 Maximum 151,134,657Probability greater than lower limit 1.09706451E8 (97.5% one sided confidence interval)
0.94 - 0.047
> 15% of study guideline
21
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Case Study - Typical WA gold mine budget
Background Surface gold mine with a EX3600 and EX1900 EX3600 is the main production unit, it is owned by the operation EX1900 is a dry hire unit that manned by casual hire Mill throughput of 2.6 Mtpa Budget is based a MineSched schedule of physicals, costing is post process. Production recording using iPads, operations have visibility Single KPI of 88,000 ounces / year
22
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Achieving Excellence with BIG DATAThe challenge of BIG data problems is that the solution is often simplified. In mining,risk analysis is simplified by the use of sensitivity analysis and discounted cash flow.Neither address the principal cause of risk in budgets, uncertainty Geology characteristics Economics; costs and revenueMining and milling production factorsMonte Carlo simulation assumes the project's factors of production have probability distributions that can be determined and sampled at will. Typically all this data is contained in production reporting systems, BIG data.
23
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Mining Market Case: Identify Issue In mine planning, forecasts have not been met and returns on investment are lower than predicted. The majority of projects (80–90%) will exceed the budget cost and will not deliver the expected benefits (Lumley and Beckman, 2009). More often, the planned production rate has not been achieved due to technical deficiencies in the planning process, planner’s optimism, and ‘strategic misrepresentation’ (deliberate deception).* Rates
Engineers are pressured to flex rate to meet a quota First principals are usually over optimistic Ignoring past performance, not representative
Example of Variance Economics Mining conditions; location, equipment and labor Processing; recovery and rate
*A proposed approach for modelling competitiveness of new surface coalmines, M.D. Budeba, J.W. Joubert, and R.C.W. Webber-Youngman 2015
24
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
300t Class All Levels per Shift
Input Process Response
3 Month Forecast
Annual Budget
LOM Plan
Strategic Schedule
Visibility and confidence + Simulation = Robust Mine Plans
25
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Manage Risk
Y1
ConstraintBoundary
Y2
Feasible Non-feasible (safe) (failed)
OutputsImprove Plan
Quality
Robustness and Reliability Analysis and
Optimization
Robust and ReliablePlan
% Unreliable% Reliable
26
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
SIMULIA Isight Workflow
27
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Production Simulation
Dig Rate by Ounces Mill Rate by Ounces Distribution of Ounces88,000 au ounces = 13% +/- 2%
Variance reduction may improve results, what causes variance?
28
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Take Control by Reducing Variance (30%)
Dig Rate (t) per Shift by Level (m)
y = -2168.1x + 18332
5000
7000
9000
11000
13000
15000
17000
19000
21000
23000
1325 1275 1225
Mean+ve sigma-ve sigmaLinear (Mean)
29
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Compare Skew vs RegressionAchieve 88,000 Ounces Au
13% +/- 2% 18% +/- 3%
30
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Meeting Target by Making Real Tactical Decisions
EX1900 EX3600 Mill Rate
Ounces
Mean Ounces 80952Standard Deviation 15257Minimum 27603Maximum 111068
Probability greater than lower limit 88000.0 (97.5%) 41% +/- 4%
31
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Mine Excellence Through Continuous ImprovementContinuous improvement may include Improve access Production management system Improved maintenance Education and training
32
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Reduce Variance by 50%, Continuous Improvement
EX1900 Class by Ounces EX3600 Class by Ounces Mill Rate by Ounces
Ounces
Mean Ounces 87890Standard Deviation 8414Minimum 63288Maximum 97581
Probability greater than lower limit 88000.0 (97.5%) 55% +/- 4%
33
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
Benefits of Simulation Studies Access to capital faster Meet your objectives higher NPV Lower risk by understanding project sensitivities. Shareholder confidence, deliver on promises
Operations Express the quality of the plan Qualifies tactical decisions Accurate plans by reducing variance High level of control in understanding variance Once realized, leveraging practices across operations
Production Management
Simulate Mean and Variance
AnalysisImprove Operation
ImplementChange
34
3DS.
COM
© Da
ssau
ltSys
tèmes
| Con
fiden
tial In
forma
tion
| 6/1/
2018
| ref.
: 3DS
_Doc
umen
t_201
4
