It is vital to graphically visualize and deal with huge data sets in oil production. Fast real time visualization assists the decision maker in the process of history matching and recovery of hydrocarbon for the oil r...
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ISBN:
(纸本)9781538675182
It is vital to graphically visualize and deal with huge data sets in oil production. Fast real time visualization assists the decision maker in the process of history matching and recovery of hydrocarbon for the oil reservoir. We provide different parallelization techniques of intensive operations for a 3-D (three dimensions) oil reservoir data visualizationtool. Our technique of MPI coarse-grain data decomposition present 38X speedup compared to 20X speedup using CUDA implementation. Combined MPI and Multi-threaded, on average gives 284X speedup over serial implementation while CUDA maximum speedup achieved is 180X with even smaller data sets.
Usefulness of graphically visualizing and manipulating large data sets in oil and gas exploration and production is as important as ever. This paper describes the development and parallelization of a multi-phase 3D oi...
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ISBN:
(纸本)9780769547497
Usefulness of graphically visualizing and manipulating large data sets in oil and gas exploration and production is as important as ever. This paper describes the development and parallelization of a multi-phase 3D oil-water reservoir visualizationtool on the IBM Cell computer and CUDA enabled GPU. An independent Oil reservoir simulator described in [1] was used to generate the pressure and oil / water saturation values over a certain period of time. The oil reservoir visualizationtool displays data grids in a 3D environment and allows the user to interact with it. Due to large speed requirements, our aim is to parallelize the computations required to interact with and visualize the grid, mainly transformation [2], zooming, camera movement [3] and compute intensive lighting model [4][5]. This tool also allows the user to playback the simulation results over a time duration and fetches data values upon mouse click at a particular grid point on a particular day. The development environments are nVIDIA CUDA and IBM Cell SDK 3.0 along with QT and OpenGL libraries. Various experiments were run on an x86 computer with nVIDIA Quadro FX 5800 GPU, and on an IBM Cell BE computer with 1 QS20 Cell blade containing two 9-core Cell processor packages. Our results indicate that the nVIDIA GPU provides on average, speed up of 67x over serial implementation and IBM Cell BE with 16 SPE SIMD implementation 32x over the serial implementation.
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