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An Approach for Characterization and Lumping of Plus Fractions of Heavy Oil

机译:重油正馏分的表征和结块方法

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Heavy-oil fluids contain large concentrations of high-molecular-weight components, including a large content of the plus fractions, such as C_(7+).rnDifferent approaches have been developed to characterize the petroleum plus fractions to improve prediction of the pseudocom-ponents properties by equations of state (EOSs).rnA method is developed in this work to split the plus fraction into single carbon numbers (SCN), generating the mole fraction and the respective molecular weight. The developed method is based on the relationships between three-parameter gamma (TPG) distribution, experimental mole fraction, molecular weight, and SCN data obtained from the literature and industrial contacts. TPG is used to fit the trend of the compositional analysis. The characterized mole distribution as a function of SCNs is generated by integrating the TPG between the limiting molecular weights (LM_w). The limiting molecular weights are determined simultaneously during the integration process by fitting the characterized and experimental mole fractions.rnThe developed method is easy to use. In addition, the approach is not dependent on the assumption that only normal carbon numbers exist in the composition resulting on fixed molecular weights for each single carbon number.rnThere are several correlations generated to predict physico-chemical properties as a function of SCNs. Those correlations have been originally developed to work with light oil. Our approach is combined with some of the correlations and is tested for heavy-oil samples to identify the ranges in which they can be applied. Two lumping schemes are used to group the SCNs into pseudocompo-nents. The properties for each pseudo-component in this work are used to predict pressure/volume/temperature (PVT) data, constant volume depletion, using the Peng-Robinson EOS (PR-EOS), and the PVTP? commercial simulator.
机译:重油流体包含高浓度的高分子量组分,其中包括大量的正馏分,例如C_(7 +)。rn已经开发了多种方法来表征石油正馏分,以改善对假组分的预测。通过状态方程(EOSs)来确定分子的性质。在这项工作中开发了一种方法,将正馏分分成单个碳原子数(SCN),生成摩尔分数和各自的分子量。所开发的方法基于三参数伽玛(TPG)分布,实验摩尔分数,分子量以及从文献和工业接触获得的SCN数据之间的关系。 TPG用于适应成分分析的趋势。通过在极限分子量(LM_w)之间积分TPG,可以生成作为SCNs函数的特征摩尔分布。通过拟合表征的摩尔分数和实验摩尔分数,可以在积分过程中同时确定极限分子量。开发的方法易于使用。另外,该方法不依赖于这样的假设,即组合物中仅存在正常的碳原子数,每个碳原子数均具有固定的分子量,因此可以预测作为SCN的理化性质。这些关联最初是为与轻油一起使用而开发的。我们的方法结合了一些相关性,并经过了重油样品测试,以确定可以应用的范围。使用两种集总方案将SCN分组为伪分量。通过使用Peng-Robinson EOS(PR-EOS)和PVTP,可以使用这项工作中每个伪组件的属性来预测压力/体积/温度(PVT)数据,恒定体积消耗。商业模拟器。

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