The density of CO₂ + crude oil mixtures is one of the most important parameters influencing CO₂ diffusion and migration in oil reservoirs. However, it would be quite time consuming to obtain comprehensive density data for CO₂ + alkane mixtures over a wide range of temperatures and pressures via experimental methods, therefore the development of a reliable model for predicting the densities of various CO₂ + alkane mixtures with high accuracy is crucial. In this paper, the parameters (m, σ, and ε/k) in the perturbed‐chain statistical associating fluid theory (PC‐SAFT) Equation of State (EoS) were optimized by correlating density data of pure n‐alkanes from heptane to nonadecane (except undecane and hexadecane). For comparison, the G‐S PC‐SAFT and HTHP PC‐SAFT EoS(s) were also employed to fit the densities of these n‐alkanes, and the results demonstrated that the PC‐SAFT EoS with the optimized parameters in this study exhibited superior accuracy. Subsequently, by correlating density data of CO₂ + alkane mixtures containing C7–C14 alkanes, the binary interaction parameter k_ij was optimized. Furthermore, for the first time, correlations between the optimized parameters (m, σ, ε/k, and k_ij) and alkane carbon number (n) were established. These correlations provided PC‐SAFT EoS with a good universality and scalability for density prediction. Using the parameters calculated from these correlations, the densities of hexadecane and saturated CO₂ + alkane mixtures containing C10–C20 alkanes were successfully predicted with relatively high accuracy. This work provides a new way for modeling the thermodynamic properties of CO₂ + alkane mixtures. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

中文翻译：

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使用PC-SAFT EoS的CO2和烷烃混合物的密度特征

CO ₂ +原油混合物的密度是影响CO ₂在油藏中扩散和迁移的最重要参数之一。但是，通过实验方法获得各种温度和压力下的CO ₂ +烷烃混合物的综合密度数据将是非常耗时的，因此开发了一种可靠的模型来预测各种CO ₂ +烷烃混合物的密度。高精度至关重要。在本文中，参数（m，σ和ε/ k）在扰动链统计缔合流体理论（PC-SAFT）中，状态方程（EoS）通过关联从庚烷到十八烷（十一烷和十六烷除外）的纯正烷烃的密度数据进行优化。为了进行比较，还使用了G‐S PC‐SAFT和HTHP PC‐SAFT EoS来拟合这些正构烷烃的密度，结果表明，本研究中具有优化参数的PC‐SAFT EoS表现出精度高。随后，通过关联包含C7–C14烷烃的CO ₂ +烷烃混合物的密度数据，优化了二元相互作用参数k _ij。此外，这是第一次，优化参数之间的相关性（m，σ，ε /确定k和k _ij）和烷烃碳数（n）。这些相关性为PC-SAFT EoS提供了良好的通用性和可扩展性，以进行密度预测。使用从这些相关性计算出的参数，可以相对较高的精度成功预测出十六烷和含有C10–C20烷烃的饱和CO ₂ +烷烃混合物的密度。这项工作为CO ₂ +烷烃混合物的热力学性质建模提供了一种新方法。©2020年化学工业协会和John Wiley＆Sons，Ltd.