一个基于gpu的工业级成分油藏模拟器

K. Esler, R. Gandham, L. Patacchini, T. Garipov, P. Panfili, F. Caresani, A. Pizzolato, A. Cominelli
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引用次数: 0

摘要

最近,图形处理单元(gpu)已经被证明可以为黑油藏模拟提供显著的性能优势,以及在成分模拟中发挥重要作用的闪速计算。基于gpu的综合成分模拟方法尚未出现,关于在黑油模拟中观察到的好处是否会在更复杂的流体描述中持续存在一些问题。我们通过扩展基于商用gpu的黑油模拟器来包含基于标准三次状态方程的成分描述,对这个问题给出了积极的答案。我们描述了我们选择最优利用GPU特性的公式的动机,包括主要变量的选择和迭代方案。然后,我们描述了一个示例扇形模型和简化的合成案例的性能结果,旨在详细检查碳氢化合物成分数量、模型尺寸以及处理器数量的缩放情况。我们最后展示了两个复杂资产模型(合成和真实)的结果,并检查了GPU生成方面的性能缩放,表明性能与GPU内存带宽密切相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A GPU-Based, Industrial Grade Compositional Reservoir Simulator
Recently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs had yet to emerge, and some questions remained as to whether the benefits observed in black-oil simulation would persist with a more complex fluid description. We present our positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state. We describe the motivations for the formulation we select to make optimal use of GPU characteristics, including choice of primary variables and iteration scheme. We then describe performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of scaling with respect to the number of hydrocarbon components and model size, as well as number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth.
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