DOWNLOAD PRESENTATIONWATCH VIDEOA High Performance Computational Platform for Simulation of Giant Reservoir Models Majdi Baddourah, M. Ehtesham Hayder, Badr Harbi, Ahmed Zawawi and Fouad Abouheit Saudi Aramco Abstract Simulation of high resolution reservoir models is useful to gain insight into oil and gas reservoirs. Nowadays, massive, comprehensive reservoir simulation models can be built with detailed geological and well log data. These models require a very large high performance computing (HPC) platform for conducting reservoir simulation. Saudi Aramco has developed a state-of-the-art simulator, GigaPOWERS, which is capable of simulating multibillion cell reservoir models. The presentation will provide an overview of challenges related to constructing HPCs and visualizing the simulation output of giant reservoir models, and how the computational platform at Saudi Aramco is designed to overcome these challenges. A large HPC platform can be designed for reservoir simulation by connecting multiple Linux clusters in a simulation grid. Such an environment can provide the necessary capacity and computational power to solve multibillion cell reservoir models. Such a simulation grid for reservoir simulation has been designed in Saudi Aramco’s Exploration and Petroleum Engineering Center (EXPEC) Computer Center. In this study, we provide the benchmark results of multiple giant fields to evaluate the performance of the Saudi Aramco simulation grid for reservoir simulation. Communication and input/output (I/O) routines in the simulator can add a considerable overhead in computation on such a computing platform. Connectivity between clusters on our simulation grid is tuned to maintain a high level of scalability in simulation. Excellent scalability results have been obtained for computations of giant simulation models on the simulation grid. Simulation models in the order of one billion cells pose a challenge to pre- and post-processing applications, which must load and process data in a reasonable time. Remote visualization, level of detail and load-on-demand algorithms were implemented in these applications, and data formats were revised to efficiently process and visualize massive simulation models.
