Mathematical Sciences: Adaptive Local Grid Refinement for Composititional Reservoir Simulation

小行星9407029 计算机硬件的进步极大地扩展了油藏模拟器的预测能力。 然而，即使具有当前的千兆次性能，执行最先进算法的机器的能力仍然远远低于需求;为了降低规划项目的风险，石油工业需要更大的计算能力，以便改进对储层性能的预测。 通过引入复杂的算法技术，如自适应局部网格细化和并行处理，可以取得进一步的进展。 在这个博士后研究计划中，我们建议将杜克大学目前正在开发的胶束-聚合物驱的自适应局部网格细化技术扩展到组分油藏模拟。 我们还建议调查该算法的并行化网络上的IBM RS6000工作站在美孚勘探和生产技术中心。

9407029 TRANGENSTEIN Advances in computer hardware have greatly extended the predictive capability of reservoir simulators. Even with current gigaflop performance, however, the capabilities of the machines executing state-of-the-art algorithms are still far below the needs; in order to reduce the risk in planning projects, the petroleum industry needs much greater computational power in order to make improved predictions of reservoir performance. Further advances can come by introducing sophisticated algorithmic techniques, such as adaptive local grid refinement and parallel processing. In this postdoctoral research program, we propose to extend adaptive local grid refinement techniques, currently being developed at Duke University for micellar- polymer flooding, to compositional reservoir simulation. We also propose to investigate the parallelization of this algorithm on a network of IBM RS6000 workstations at Mobil Exploration and Production Technical Center.