Majdi Baddourah

Challenges in High Performance Computing for Reservoir Simulation

High resolution reservoir modeling is necessary to analyze complex flow phenomena in the reservoir. As more powerful computing platforms are becoming available, simulation engineers are building larger high resolution reservoir models to study giant fields. Complexities in simulation algorithms and memory contentions are challenges to efficient use of emerging computing platforms. As new tools are available and performance of hardware and software improves, it is important to reevaluate and revise implementation details of the simulator to maintain a high level of scalability on the large number of processors. There is enormous potential in emerging technologies, such as graphic processing units (GPUs), but currently, the lack of development tools restricts their adaptation in high performance computing. Researchers in many areas of computational fluid dynamics have been able to achieve a very high level of computational performance. Such a high level is yet to be achieved in reservoir simulation. In this study, we review computational difficulties in high performance computing related to reservoir simulation and examine how improvements can be made by use of emerging technologies in this class of problems. We discuss our efforts towards improving communication and input/output (I/O) algorithms in our reservoir simulator. We also evaluate high speed interconnections, various communication libraries, etc., for parallel computations and examine how simulation performance can be improved on the latest multi-core processors. Benchmark results of various computational and I/O kernels and a summary of actual simulation results will be reviewed to illustrate current challenges and the near term outlook of high performance reservoir simulation studies.

Towards Building Scalable Grid Computing Environments for Reservoir Simulation

Standard high performance clusters (HPC) are being extensively used for solving computationally intensive problems in various scientific fields, one of which is the field of reservoir simulation. In our work, we expand the conventional HPC systems to run larger reservoir simulations on a heterogeneous grid of HPCs. This expansion is accomplished by developing a unique domain decomposition technique to achieve higher load balancing and computational efficiency on the grid. The decomposer works as an adaptive detector of the available hardware resources on the grid, learns about the different processor speeds and adjusts its load distribution among the CPUs. As a result, our grid implementation for reservoir simulation outperforms the conventional methods of domain decomposition, which are designed to run on homogenous set of processors in a cluster.

Poster: high performance computing reservoir simulation in the oil industry

Large reservoir simulation is computationally challenging. High resolution reservoir models provide necessary realism in heterogeneous reservoirs. Large number of simulations of such models is necessary in reducing uncertainties in reservoir characterizations and creating efficient field development plans. To meet such challenges, Saudi Aramco, the worlds leading oil producing company, developed a parallel reservoir simulator, POWERS [1], and built a computing center currently hosting computing power of over 300 teraflops [2]. Both reservoir simulator and computing center are world leaders in the oil industry. In this paper we review some of most innovative HPC works in the oil industry. We review computational characteristics of the POWERS simulator and benchmark results for computations of large reservoir simulation models throughout the past few years. We also share studies to enhance POWERS in various aspects such as GPU usage to solve critical computing blocks, MPI tuning, and reducing the eco footprint of the simulator.

Selecting a CPU for Reservoir Simulation Optimized for Cost, Energy and Performance

The model validation with the actual experimental results will be shared. This paper presents guidelines for decisions on which CPU in a product family will meet an organization’s compute requirements with a balance of power consumption, performance, cost and user’s experience

Toward building green computing environment for reservoir simulation

In this paper, we analyze the level of power consumption for running large scale hydrocarbonreservoir simulation on high performance clusters (HPC), which may impact the data center design and the overall environment for running massive simulation models. We derive energy consumption from simulation run time and the power needed to keep associated processors running. We then propose a metric that ties reservoir simulation to Power Usage Effectiveness (PUE) factor based on energy consumption per-node. This factor works as the efficiency target that can be adjusted to balance execution time and power consumption.