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Mitigating the Energy Crisis using Simulation for Enhanced Oil Recovery Analyzing oil fields grain by grain

Mitigating the Energy Crisis using Simulation for Enhanced Oil Recovery Analyzing oil fields grain by grain. David Holmes, John Williams Civil and Environmental Engineering Massachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll Research

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Mitigating the Energy Crisis using Simulation for Enhanced Oil Recovery Analyzing oil fields grain by grain

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  1. Mitigating the Energy Crisis usingSimulation for Enhanced Oil RecoveryAnalyzing oil fields grain by grain David Holmes, John Williams Civil and Environmental EngineeringMassachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll Research December 9th, 2008 www.milner-photo.com/industrial/home.html

  2. Outline • Enhanced Oil Recovery • Problem Statement • Modeling Challenges • Developing an Advanced Simulation Framework • Simulation Challenges for Multi-Core • Dynamic Execution Management • Testing and Applications • Conclusions

  3. Enhanced Oil RecoveryProblem Statement Oil Saturated Pores pubs.usgs.gov/dds/dds-033/USGS_3D/ssx_txt/all.htm • Primary Development 20 – 40% Recovery • Existing EOR Such as • Water Flooding Optimistically an • Gas Injection Additional 10 – 20% • Chemical Injection Recovery • Thermal Stimulation www.llnl.gov/str/November01/Kirkendall.html

  4. Enhanced Oil RecoveryProblem Statement • 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 http://www.shmolnick.com/images/traffic_jam_web.jpg http://photo.net/photodb/photo?photo_id=5219385&size=lg

  5. Enhanced Oil RecoveryModeling Challenges • Multi-phase fluid flow at the pore scale

  6. 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 www.co2crc.com.au/aboutgeo/storage.html

  7. 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 www.thewaterexperts.com/welldevelopment.htm

  8. 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

  9. Enhanced Oil RecoveryModeling Challenges http://www.netl.doe.gov/technologies/oil-gas/FutureSupply/MethaneHydrates/projects/DOEProjects/MH_43067GasHydSediments.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 • Understanding the hydro-fracturing of rocks • Understanding the mechanisms of sand production and borehole collapse • Carbon sequestration and hydrate mining

  10. 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 • Carbon sequestration and hydrate mining • Architecting integrated multi-physics software systems that can run on multi-core/multi-machine architectures

  11. 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

  12. Developing an Advanced Simulation FrameworkSimulation Challenges for Multi-Core Spatial Reasoning and Task Distribution Domain Decomposition Domain Distribution (Predictive Load Balance) (Events Based Load Balance)

  13. Developing an Advanced Simulation FrameworkSimulation Challenges for Multi-Core Dynamic Evolution of the Numerical Task Adaptive Remeshing Removal of Elements Outside Critical Zone Variable Free Surface on a Grid

  14. Developing an Advanced Simulation FrameworkDynamic Execution Management Microsoft’s Concurrency and Coordination Runtime (CCR) • Acknowledgements George Chrysanthakopoulos Henrik Nielsen • Primary Concurrency Tools • Port • Receiver

  15. Developing an Advanced Simulation FrameworkDynamic Execution Management The Developed Dispatch Mechanism

  16. 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

  17. Testing and ApplicationsDEM and Particle Methods Focus on Grain to Macro Scale Analysis Goal is to handle Multi-Physics, Multi-Scale FIXED GRID MOVING PARTICLE METHODS Molecular Dynamics Particle Methods DEM FD FEM Focus on Macro and Grain Scale Physical Scale Macro Meso Grain Nano

  18. Testing and Applications

  19. Testing and ApplicationsSpeed tests carried out on a Dell Server PE2900with 8-core Intel Xeon E5345 CPU Speed-Up Efficiency

  20. Testing and ApplicationsFalling drop example ~200 000 particles, 10 000 time steps

  21. Testing and ApplicationsRayleigh-Taylor Instability test ~500 000 particles, 25 000 time steps

  22. Testing and ApplicationsSimulation of water flooding – Goal is to optimize recovered oil by “designing” fluids Non-wetting water phase (blue) invading wetting oil phase (red) A Wetting water phase (blue) invading non-wetting oil phase (red) B

  23. Testing and ApplicationsCalibration of large filed scale models based on a better understanding of the pore scale phenomena K. Geel, Delft University of Technology

  24. 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 www.llnl.gov/str/November01/Kirkendall.html http://openlearn.open.ac.uk/file.php/2292/formats/print.htm

  25. Conclusions

  26. Developing an Advanced Simulation FrameworkDynamic Execution Management • Conventional CCR example of a repeated scatter-gather Scatter Parallel Gather

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