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首页> 外文期刊>American journal of applied sciences >ASSISTED HISTORY MATCHING FOR PETROLEUM RESERVOIRS IN THE SOCIAL COMPUTING ERA
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ASSISTED HISTORY MATCHING FOR PETROLEUM RESERVOIRS IN THE SOCIAL COMPUTING ERA

机译：社会计算时代的石油储层辅助历史匹配

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摘要

The exploitation strategy of hydrocarbon reservoirs can be technically and economically optimized only if a reliable numerical model of the reservoir under investigation is available to predict the system response for different production scenarios. A numerical model can be reasonably trustworthy after calibration only, which means the model has at least proved its ability to reproduce the historical behavior of the reservoir it represents. The calibration procedure, also known as history matching, is the most time consuming phase in a reservoir study workflow. Over the last decades several methods, classified as Assisted History Matching (AHM), have been proposed for a partial automation of the model calibration procedure. Meta-heuristic methods have been used to iteratively reduce the misfit between simulated and historical data. However, the main limit for the application of these algorithms is the amount of computational time necessary for the evaluation of the objective function, thus for the simulation runs. On the other hand, the new trend on collective computing offers a solution to CPU intensive tasks by distributing the work among several computers located in different places but globally connected through the World Wide Web. In this study a novel workflow for assisted history matching is proposed. The results proved that this workflow provides better and more representative solutions in a fraction of the time needed by traditional approaches.
机译：仅当可使用正在研究的储层的可靠数值模型来预测不同生产场景的系统响应时，才能在技术和经济上优化油气藏的开发策略。仅在校准后，数值模型才可以合理地值得信赖，这意味着该模型至少证明了其能够再现其代表的储层历史行为的能力。校准程序（也称为历史匹配）是储层研究工作流程中最耗时的阶段。在过去的几十年中，已经提出了几种分类为辅助历史匹配（AHM）的方法，用于模型校准过程的部分自动化。元启发式方法已用于迭代减少模拟数据和历史数据之间的不匹配。但是，应用这些算法的主要限制是评估目标函数（因此需要进行仿真）所需的计算时间。另一方面，集体计算的新趋势是通过将工作分配到位于不同位置但通过万维网进行全球连接的几台计算机之间来为CPU密集型任务提供解决方案。在这项研究中，提出了一种用于辅助历史匹配的新颖工作流程。结果证明，该工作流程在传统方法所需的一小部分时间内提供了更好，更具代表性的解决方案。

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