A novel software tool has been developed by Process Ecology to explore trade-offs in energy, emissions, water and cost in SAGD facilities

1. An Innovative Approach to Evaluate
New Technologies in SAGD Plants
Alberto Alva-Argaez, Maryam Mahmoudkhani
COSIA –AIEES Water Conference. March 22-23, 2016

2. Outline
• Introduction: Oil Sands – SAGD facilities
• Objective
• Integrated approach in process optimization
and technology evaluation
• How to evaluate GHG, water and operational
cost trade-offs?
• “Surmont TriOpti” Tool
• Next steps
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3. Steam Assisted Gravity Drainage (SAGD)
• Thermal production technology to extract bitumen
from Alberta’s subsurface oil sands deposits
3
Wellpad Oil Treating
De-oiling
Water Treating:
Lime-Softening /
Evaporator
Steam
Generation
(OTSG)
Emulsion:
Bitumen+ Water
Produced Water
Deoiled
WaterBFW
Steam
Make-up
Water
Waste to
disposal
Sales Oil
DiluentDiluent

4. 4
Objective
• Better tools required:
– for combined analysis of water and energy systems,
including operating costs and environmental
impacts (i.e. trade-off)
– to identify opportunities for reducing OpEx
– to improve methods to holistically evaluate new
technology

5. 5
Why trade-off analysis for OpEx-GHG-Water?
• Highly integrated hydrocarbon and water treatment
units
• High degree of heat integration
– Optimizing for energy consumption may not result in the
best projects due to the impacts on water consumption and
overall operating cost
– Increasing water recycle may increase energy usage, (i.e.
greenhouse gas emissions)
• Identifying the best environmental and economic
tradeoff requires an “Integrated Approach”

6. 6
Simulation vs. Optimization
Find shortest path between A and B

7. 7
Simulation vs. Optimization
– Square system of
equations
– No degrees of
freedom available
– User specifies all
parameters
• E.g. split fraction for
recycle vs disposal
– More variables than
equations
– Many degrees of freedom
may be available
– Solution found against an
objective function
– Optimal values for
parameters to minimize
objective function

8. 8
Simulator vs. Surmont TriOpti
– General purpose tool-
compromise
– Fluid characterization-
pseudocomponents
– No cost elements,
environmental metrics
– Unable to enforce
constraints such as >= or
<=. Only trial and error
– Specifically designed for SAGD
– Fluid characterization fit for
purpose:
• Bitumen: Tailor made models
• Water: Rigorous correlations
• Gas: Peng-Robinson
• Ions: Separate species
– Fewer components- efficient
calculations
– Integrated cost and
environmental metrics
– Optimization-based. Constraints
definition built in

9. 9
Optimization approach
1
2
PW Disposal
1
2
PW Disposal
Optimize (min OpEx)
Decision variables – water flow rates

10. Optimization models
– GAMS or Matlab platform and files
• UMIST
• U of C Collaboration
Challenges:
- Update/ maintain models
- Interpretation of results
- Convergence of general solvers
- Difficult to share results
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11. Our approach: Surmont TriOpti
- TriOpti is a custom software with a fully flexible, modern user interface to define
process models, provide input parameters and visualize results.
- Robust optimization engine based on Process Ecology’s in-house algorithms
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Simple user interface and workflows

12. Software Elements
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Graphical User Interface
Simulation- calculation engine
“Xtrema Engine”
Optimization algorithms
p-graphs
Thermodynamics
Mass/ Energy balances Unit Operations
Physical properties

13. System Boundary
Oil
Produced
water
Makeup
water
Skim Tank
ORF/IGF
WLS/AF
WAC
OTSG
Disposal
Steam
Blowdown
recycle
BFW
FWKO Treater
Emulsion
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14. Models only as detailed as necessary
• Mass and Energy Balances
• Customized phase equilibrium
• Reduced “Component” slate (bitumen, diluent, fuel,
water quality)
• Simple models for treatment technologies
• Pump energy, chemicals usage and disposal cost-
simple functions of flow rate
• Pigging frequency function of blowdown recycle rate
or inlet BFW quality to OTSG
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15. Surmont TriOpti
• Efficient evaluation of new technologies
• Identify novel opportunities and alternatives for opex
reduction
• Less trial and error in simulations – more process
understanding
• Platform to search for better processes
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