Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

• Published in: IEEE access Vol. 8; pp. 212280 - 212297
• Main Authors: El-Moursy, Ali A., Sibai, Fadi N., Khaled, Hanan, Nassar, Salwa M., Taher, Mohamed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Communications traffic; Computational modeling; Data processing; Data visualization; data visualization tool; Decision analysis; Distributed memory; Gas pressure; HPC; hybrid (distributed/shared)-memoryparallel programming; Massive data points; MPI; multi-threading; Oils; OpenStack; Parallel processing; Petroleum production; Production management; Reservoirs; Scientific visualization; Statistical analysis; Virtual environments; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037731

The main goal of oil reservoir management is to provide more efficient, price-effective and environmentally more secure oil production. Oil production management includes an accurate characterization of the reservoir and strategies that involve interactions between reservoir data and human assessment. Hence, it is important to graphically visualize and handle massive data sets of oil and gas pressure / saturation levels to help decision makers in statistical analysis, history matching and recovery of hydrocarbons of the reservoir. In this article, we experimentally study the parallelization of intensive computation for a 3-D (three dimensional) oil reservoir data visualization tool. For this tool, we develop and implement a transformation and lighting model to visualize and react with the grid. Herein, we propose a hybrid (shared memory and distributed memory) parallelization technique to adapt with the data processing scalability. We tested these implementations on OpenStack Cloud Virtual Cluster. Our results indicate that although the virtual platform adds overhead for running parallel implementations, utilizing knowledge of the VM location on the compute host and network traffic among VMs to deploy the virtual environment can achieve significant performance enhancements. Hybrid parallel implementation using large data size can achieve <inline-formula> <tex-math notation="LaTeX">70\times </tex-math></inline-formula> speedup over serial execution without owning a costly HPC infrastructure as the conventional parallel processing deployment model.