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![Page 1: David Holmes, John Williams Civil and Environmental Engineering Massachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll.](https://reader034.fdocuments.in/reader034/viewer/2022051613/551478b2550346ea6e8b458c/html5/thumbnails/1.jpg)
David Holmes, John WilliamsCivil and Environmental EngineeringMassachusetts Institute of Technology
Peter TilkeMathematics and Modeling DepartmentSchlumberger-Doll Research
December 9th, 2008
Mitigating the Energy Crisis usingSimulation for Enhanced Oil RecoveryAnalyzing oil fields grain by grain
www.milner-photo.com/industrial/home.html
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Enhanced Oil Recovery– Problem Statement– Modeling Challenges
Developing an Advanced Simulation Framework– Simulation Challenges for Multi-Core– Dynamic Execution Management
Testing and Applications Conclusions
Outline
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Primary Development 20 – 40% Recovery
Existing EOR Such as– Water Flooding Optimistically an– Gas Injection Additional 10 – 20%– Chemical Injection Recovery– Thermal Stimulation
Enhanced Oil RecoveryProblem Statement
www.llnl.gov/str/November01/Kirkendall.html
pubs.usgs.gov/dds/dds-033/
USGS_3D/ssx_txt/all.htm
Oil Saturated
Pores
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The Department of Energy (DOE) estimates that using ‘Next Generation EOR’ the United States could generate an additional 240 billion barrels of recoverable oil resources
This corresponds to approximately 30 years supply at current consumption
Developing new EOR is critical to maintaining our way of life
Enhanced Oil RecoveryProblem Statement
http://photo.net/photodb/photo?photo_id=5219385&size=lg http://www.shmolnick.com/images/traffic_jam_web.jpg
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Enhanced Oil RecoveryModeling Challenges
Multi-phase fluid flow at the pore scale
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Enhanced Oil RecoveryModeling Challenges
www.co2crc.com.au/aboutgeo/storage.html
Multi-phase fluid flow at the pore scale Interpretation and calibration of down-
hole measurement techniques such as electrical resistivity and acoustic wave propagation
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Enhanced Oil RecoveryModeling Challenges
www.thewaterexperts.com/
welldevelopment.htm
Multi-phase fluid flow at the pore scale Interpretation and calibration of down-
hole measurement techniques such as electrical resistivity and acoustic wave propagation
Understanding the hydro-fracturing of rocks
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Enhanced Oil RecoveryModeling Challenges
Multi-phase fluid flow at the pore scale Interpretation and calibration of down-
hole measurement techniques such as electrical resistivity and acoustic wave propagation
Understanding the hydro-fracturing of rocks
Understanding the mechanisms of sand production and borehole collapse
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Multi-phase fluid flow at the pore scale Interpretation and calibration of down-
hole measurement techniques such as electrical resistivity and acoustic wave propagation
Understanding the hydro-fracturing of rocks
Understanding the mechanisms of sand production and borehole collapse
Carbon sequestration and hydrate mining
Enhanced Oil RecoveryModeling Challenges
http://www.netl.doe.gov/technologies/oil-gas/
FutureSupply/MethaneHydrates/projects/DOEProjects/
MH_43067GasHydSediments.html
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Multi-phase fluid flow at the pore scale Interpretation and calibration of down-
hole measurement techniques such as electrical resistivity and acoustic wave propagation
Understanding the hydro-fracturing of rocks
Understanding the mechanisms of sand production and borehole collapse
Carbon sequestration and hydrate mining Architecting integrated multi-physics
software systems that can run on multi-core/multi-machine architectures
Enhanced Oil RecoveryModeling Challenges
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Developing an Advanced Simulation FrameworkSimulation Challenges for Multi-Core
Concurrency Packages Conventional parallel simulation implementations use MPI A powerful new library for multi-core is Microsoft’s CCR Further room for generalization for simulation applications
Challenges to Concurrency General challenges
– Synchronization– Thread safety– Load balance
Challenges unique to simulation in parallel– Spatial reasoning and task distribution– Dynamic evolution of numerical tasks
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Spatial Reasoning and Task DistributionDomain Decomposition Domain Distribution(Predictive Load Balance) (Events Based Load Balance)
Developing an Advanced Simulation FrameworkSimulation Challenges for Multi-Core
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Developing an Advanced Simulation FrameworkSimulation Challenges for Multi-Core
Dynamic Evolution of the Numerical TaskAdaptive Remeshing Removal of Elements Outside Critical Zone
Variable Free Surface on a Grid
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Developing an Advanced Simulation FrameworkDynamic Execution Management
Microsoft’s Concurrency and Coordination Runtime (CCR)
AcknowledgementsGeorge Chrysanthakopoulos Henrik Nielsen
Primary Concurrency Tools– Port
– Receiver
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Developing an Advanced Simulation FrameworkDynamic Execution Management
The Developed Dispatch Mechanism
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Developing an Advanced Simulation FrameworkDynamic Execution Management
Advantages– Perfectly load balanced to within required operations on 1 data point– Accommodates any CPU number– Accommodates variable CPU efficiency/availability and remains load balanced
Programming Challenges– Dispatch must know when all data has been received– Dispatch must recognize when data has been distributed– All processes must complete before finalization
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Testing and ApplicationsDEM and Particle Methods Focus on Grain to Macro Scale Analysis
FD Particle Methods DEM
FIXED GRID MOVING PARTICLE METHODS
FEM
Goal is to handle Multi-Physics, Multi-Scale
NanoGrain
Focus on Macro and Grain Scale
MesoMacroPhysicalScale
Molecular Dynamics
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Testing and Applications
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Speed-Up Efficiency
Testing and ApplicationsSpeed tests carried out on a Dell Server PE2900with 8-core Intel Xeon E5345 CPU
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Testing and ApplicationsFalling drop example ~200 000 particles, 10 000 time steps
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Testing and ApplicationsRayleigh-Taylor Instability test ~500 000 particles, 25 000 time steps
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Testing and ApplicationsSimulation of water flooding – Goal is to optimize recovered oil by “designing” fluids
A
B
Non-wetting water phase (blue) invading wetting oil phase (red)
Wetting water phase (blue) invading non-wetting oil phase (red)
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Testing and ApplicationsCalibration of large filed scale models based on a better understanding of the pore scale phenomena
K. Geel, Delft University of Technology
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Testing and ApplicationsCarbon sequestration and hydrates mining
Multi-phase modeling of gas/liquid/solid interactions to allow– Reduction of green-house gas
emissions through carbon sequestration– Reduction of the environmental impact
of unstable sub-sea methane deposits– Extraction of methane as an alternate
fuel source
http://openlearn.open.ac.uk/file.php/2292/formats/print.htm
www.llnl.gov/str/November01/Kirkendall.html
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Conclusions
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Developing an Advanced Simulation FrameworkDynamic Execution Management
Conventional CCR example of a repeated scatter-gather
Scatter
Parallel
Gather