modeling and optimization of in-situ oil production
TRANSCRIPT
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Modeling and Optimization of Wells Scheduling for In-Situ Oil Production
Stream Systems Ltd
AnyLogic Conference 2015
Philadelphia, PA
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Context – Oil Sands
Oil Sands: Natural mixture of sand + oil + water + others
3 main countries In-Situ Capex: 34 billion CAD in 2014 High operational cost In-Situ production > 1.3m bpd
Oil price
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In-Situ Technology
• Applying heat (steam) to oil reservoirs beneath the earth's surface to warm the bitumen so it can be pumped to the surface through recovery wells.
• Two common types of in-situ petroleum production: SAGD & CSS
Steam-Assisted Gravity Drainage (SAGD) Oil Sands Reservoir
Stage 1SteamInjection
Stage 2SoakPhase
Stage 3Production
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Systemic View
Steam
Reservoir Wells
Pads
Emulsion
CPF
Emulsion
OilGas
Disposed WaterFresh Water
Emulsion
100 wells
Unpredictable reservoir
response
Pipeline network
Time lagged feedback
loops
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Complexity
Surface & Subsurface data/models separately
Methodological approach No integrated approach Spreadsheets, lots of it! No variability/scenario analysis
High Complexity!
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Modeling Approach
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Close the Loops Above to Below Ground and Back
Surface & Subsurface Data Input
Production Profiles
DB, Spreadsheets, Text and CSV files
Operational Data
Artificially Generated
Reliability
SeasonalityMaintenanceLayout
CPF Configuration
Infrastructure
Quality
Expected Production
Steam RequirementsPhysical & Chemical Data
Steam
Reservoir Wells
Pads
Emulsion
CPF
Emulsion
OilGas
Disposed WaterFresh Water
Emulsion
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Why AnyLogic Agent-Based and Discrete Event Approach Fluids Library Easy to integrate with external data sources High Performance External Java libraries to manage additional
calculations
Simulation & Visualization
Engine
External Processing Engine
Data Management
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6CURVES
VARIABLE FLOW
120WELLS
14VARIABLES
70PARAMETERS
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100MERGERS & SPLITS
Emulsion Flow Mergers and Splits
260OTHER COMPONENTS
24HOURS
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Advantages of the Approach
Dynamic populations Fluid modeling Tracking of all batches in the model Quality calculations Advanced decision algorithms
Scheduling Backward calculations Reliability
Multiple scenario analysis
Optimization
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Summary
Systemic Approach
Deal with Complexity
Ripple and Timing Effects
Experimentation Platform
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Team of Collaborators Manoochehr Akhlaghnia, PhD. Alistair Wright, PhD. Dumitru Cernelev, P.Eng, MBA. Birgit Juergensen, Dipl.Ing.Oec Alvaro Gil, M.Sc. Industrial Partners
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Thanks for your attention
Q&A Session