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David Holmes, John Williams Civil and Environmental Engineering Massachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll.

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Presentation on theme: "David Holmes, John Williams Civil and Environmental Engineering Massachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll."— Presentation transcript:

1 David Holmes, John Williams Civil and Environmental Engineering Massachusetts Institute of Technology Peter Tilke Mathematics and Modeling Department Schlumberger-Doll Research December 9 th, 2008 Mitigating the Energy Crisis using Simulation for Enhanced Oil Recovery Analyzing oil fields grain by grain

2 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

3 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 Recovery Problem Statement pubs.usgs.gov/dds/dds- 033/USGS_3D/ssx_txt/all.htm Oil Saturated Pores

4 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 Recovery Problem Statement

5 Enhanced Oil Recovery Modeling Challenges Multi-phase fluid flow at the pore scale

6 Enhanced Oil Recovery Modeling 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

7 Enhanced Oil Recovery Modeling Challenges lopment.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

8 Enhanced Oil Recovery Modeling 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 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 Recovery Modeling Challenges gas/FutureSupply/MethaneHydrates/projects/DO EProjects/MH_43067GasHydSediments.html

10 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 Recovery Modeling Challenges

11 Developing an Advanced Simulation Framework Simulation Challenges for Multi-Core Concurrency Packages Conventional parallel simulation implementations use MPI A powerful new library for multi-core is Microsofts 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 Spatial Reasoning and Task Distribution Domain Decomposition Domain Distribution (Predictive Load Balance) (Events Based Load Balance) Developing an Advanced Simulation Framework Simulation Challenges for Multi-Core

13 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 Framework Dynamic Execution Management Microsofts Concurrency and Coordination Runtime (CCR) Acknowledgements George ChrysanthakopoulosHenrik Nielsen Primary Concurrency Tools –Port –Receiver

15 Developing an Advanced Simulation Framework Dynamic Execution Management The Developed Dispatch Mechanism

16 Developing an Advanced Simulation Framework Dynamic 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 Applications DEM and Particle Methods Focus on Grain to Macro Scale Analysis FD Particle MethodsDEM FIXED GRIDMOVING PARTICLE METHODS FEM Goal is to handle Multi-Physics, Multi-Scale NanoGrain Focus on Macro and Grain Scale MesoMacroPhysical Scale Molecular Dynamics

18 Testing and Applications

19 Speed-Up Efficiency Testing and Applications Speed tests carried out on a Dell Server PE2900 with 8-core Intel Xeon E5345 CPU

20 Testing and Applications Falling drop example ~ particles, time steps

21 Testing and Applications Rayleigh-Taylor Instability test ~ particles, time steps

22 Testing and Applications Simulation 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)

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

24 Testing and Applications Carbon 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

25 Conclusions

26 Developing an Advanced Simulation Framework Dynamic Execution Management Conventional CCR example of a repeated scatter-gather Scatter Parallel Gather


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