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Sensitivities to Component Characterizations of Heavy Oil Viscosity in Numerical Reservoir Simulation of Steam-Injection Processes

机译:蒸汽注入过程数值模拟中重油粘度组分表征的敏感性

摘要

This work examines heavy oil viscosity modelling during simulation of steam injection processes, such as steam-line-drive and SAGD, and the sensitivity of oil recovery predictions to the uncertainty in the oil viscosity. Analytical models to predict the sensitivity have been developed, confirmed by numerical simulation.udHeavy oil compositional component viscosities are modelled with the Free Volume model. The model is extended in this thesis to estimate the viscosities of long-chain n-alkanes from C6H14 to C45H92 within an accuracy of 10% in the temperature range 27 to 300 °C (80 to 575 °F) and pressure range 0.1 to 120 MPa (14.5 to 17,400 psi). It estimates viscosities of long-chain n-alkanes up to C64H130 to within 30%. Extrapolated Free Volume molecular characteristic parameters, optimised based on available viscosity measurements for n-alkanes up to C64H130, are provided, and are the recommended values for use in heavy oil simulation.udA heavy pseudo-component, representing a combination of asphaltenes and resins, which are the compounds responsible for the high viscosities observed in heavy oil, is characterised in terms of molecular weight, shape and activation energy for viscous flow. A method to predict its viscosity as a function of its physical properties, pressure and temperature, using the Free Volume model, is demonstrated.udA density model based on the Tait model is extended, to predict the long-chain heavy oil compositional component densities within an accuracy of 3%, in the same temperature and pressure ranges as above.udA grouping procedure is demonstrated to achieve oil recovery results comparable to a 24-component simulation case, using two pseudo-components. Key is the mixing equation used to calculate the oil phase viscosity as components are grouped. The Arrhenius mixing equation is evaluated for accuracy in predicting hydrocarbon mixture viscosities. Guidelines for accurate use are provided, while mixtures with CO2 are shown to require a different method.
机译:这项工作研究了在蒸汽注入过程(例如蒸汽管线驱动和SAGD)的模拟过程中的重油粘度建模,以及油回收预测对油粘度不确定性的敏感性。已经开发了用于预测灵敏度的分析模型,并通过数值模拟进行了验证。 ud使用自由体积模型对重油成分组分的粘度进行建模。本文扩展了该模型,以估算在温度范围27至300°C(80至575°F)和压力范围0.1至120范围内,C6H14至C45H92的长链正构烷烃的粘度在10%的精度范围内MPa(14.5至17,400 psi)。它估计高达C64H130的长链正构烷烃的粘度在30%以内。提供了外推的自由体积分子特征参数,该参数基于对高达C64H130的正构烷烃的可用粘度测量进行了优化,并且是在重油模拟中使用的推荐值。 ud重的假组分,代表沥青质和树脂的组合在重油中观察到的是导致高粘度的化合物,其特征在于分子量,形状和粘性流的活化能。展示了一种使用自由体积模型预测其粘度随其物理性质,压力和温度变化的方法。 ud扩展了基于Tait模型的密度模型,以预测长链重油组成组分的密度在与上述相同的温度和压力范围内,精度为3%。 udA分组程序证明了使用两个伪组分可以达到与24组分模拟情况相当的采油结果。关键是当组分分组时用于计算油相粘度的混合方程式。对Arrhenius混合方程式进行评估,以预测烃混合物粘度的准确性。提供了准确使用的准则,但显示与二氧化碳的混合物需要使用不同的方法。

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    de La Porte Jacoba;

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  • 年度 2013
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  • 原文格式 PDF
  • 正文语种 eng
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