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Modular tool for the simulation of compressor trains for oil and gas applications

机译:用于仿真油气应用压缩机列车的模块化工具

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Recently, in the oil and gas extraction and transportation field, much attention has been paid both to increase efficiency and to reduce the environmental impact of the extraction techniques that, by now, consists mainly on Enhanced Oil Recovery processes based on gas or water injection into the reservoirs. Thus, compressor trains are a crucial part of the overall plant, and they require precise performance estimation during the whole oilfield lifespan, when production rates and compression demands significantly change. For this reason, in compression plant design and in-service behavior prediction, modular simulation codes turns out to be the best choice respect to tools for specific plant configuration, since they provide flexibility without losing accuracy. In this paper, a new modular tool for compression plant simulation is described; it is based on a wide database of centrifugal compressors and a library of elementary components that can be freely assembled to build any plant's configuration, regardless of its layout. The code's numerical solver is the implementation of a trust-region Gauss-Newton method, called TRESNEI, which possess a larger convergence region than standard Newton methods. The performance of the code has been tested on two compression train arrangements with both series and parallel-mounted compressors; comparison with the solution of the test cases obtained with a dedicated pre-existing in-house code, shows a good matching between the results. Computational speed and robustness of the new code is also shown.
机译:最近,在石油和天然气提取和运输领域,既有效率均致力于提高效率,并降低提取技术的环境影响,目前,主要是基于气体或注水进入的增强的采油过程水库。因此,压缩机列车是整个植物的重要部分,当生产率和压缩需求显着改变时,它们需要精确的性能估计。出于这个原因,在压缩设备设计和在职行为预测中,模块化仿真代码结果是对特定工厂配置的工具的最佳选择方面,因为它们提供了灵活性而不会降低精度。本文描述了一种新的压缩工厂模拟模块化工具;它基于一系列离心式压缩机数据库和一个可以自由组装以构建任何工厂的配置的基本组件库,无论其布局如何。代码的数值求解器是执行称为Tresnei的信任区域Gauss-Newton方法,该方法具有比标准牛顿方法更大的收敛区域。代码的性能已经在两个压缩列车布置上进行了测试,串联和并联压缩机;与专用预先存在的内部代码一起获得的测试用例的解决方案的比较显示了结果之间的良好匹配。还显示了新代码的计算速度和鲁棒性。

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