The application of static load balancers in parallel compositional reservoir simulation on distributed memory system
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2015 Elsevier B.V. Compositional reservoir simulation depicts the complex behaviors of all the components in gaseous, liquid, and oil phases. It helps to understand the dynamic changes in reservoirs. Parallel computing is implemented to speed up simulation in large scale fields. However, there is still many challenges in obtaining efficient and cost-effective parallel reservoir simulation. Load imbalance on processors in the parallel machine is a major problem and it severely affects the performance of parallel implementation in compositional reservoir simulators. This article presents a new approach to the reduction of load imbalance among processors in large scale parallel compositional reservoir simulation. The approach is based on graph partitioning techniques: Metis partitioning and spectral partitioning. These techniques treat the simulation grid, or the mesh, as a graph constituted by vertices and edges, and then partition the graph into smaller domains. Metis and spectral partitioning techniques are advantageous because they take into account the potential computational load of each grid block in the mesh and generate smaller partitions for heavy computational load areas and larger partitions for light computational load areas. In our case, the computational load is represented by transmissibility. After new partitions are generated, each of them is assigned to a processor in the parallel machine and new parallel reservoir simulation can be conducted. Traditionally, the intuitive 2D decomposition is frequently used to partition the simulation grid into small rectangles, and this is a major source of load imbalance. The performance of parallel compositional simulation based on our partitioning techniques is compared with the most commonly used 2D decomposition and it is found that load imbalance in our new simulations is reduced when compared with the traditional 2D decomposition. This study improves the efficiency of compositional simulation and eventually makes it more cost-effective for hydrocarbon simulation on mega-scale reservoir models.
