Measurement and Modeling of Minimum Miscibility Pressure: A State-of-the-Art Review

混溶性 旋节分解 流量(数学) 相图 计算机科学 临界点(数学) 统计物理学 混乱 混合(物理) 机械 热力学 材料科学 相(物质) 数学 化学 物理 心理学 聚合物 量子力学 数学分析 复合材料 有机化学 精神分析
作者
Birol Dindoruk,Russell T. Johns,Franklin M. Orr
出处
期刊:SPE reservoir evaluation & engineering [Society of Petroleum Engineers]
卷期号:24 (02): 367-389 被引量:61
标识
DOI:10.2118/200462-pa
摘要

Summary This paper gives a critical review of miscibility-measurement techniques published in the open literature along with recommendations and lessons learned. Many of these published methods violate the inherent assumptions for multicontact miscibility (MCM). The confusion often arises from a failure to distinguish between first-contact miscibility (FCM), in which two fluids can be mixed in all proportions without forming two phases, and MCM, in which fluid compositions that arise during the flow of two phases in a porous medium approach a specific critical point within the constraints of the MCM definition. There are many analytical, numerical, correlational, and experimental methods available to estimate the minimum miscibility pressure (MMP) for MCM flow. The numerous available methods, some of which are quite inexpensive, have caused significant misunderstandings in the literature and in practice regarding their ability to estimate MMP. Our experience has shown that the best methods are those that honor the multicontact process (MCM), in which flow interacts with phase behavior in a prescribed way. Good methods that achieve this are slimtube experiments, detailed slimtube simulations, multiple-mixing-cell calculation methods, and the method of characteristics (MOC). Techniques such as the rising-bubble-apparatus (RBA) and vanishing-interfacial-tension (IFT) (VIT) experiments are subject to significant uncertainties, although they can still provide useful information. Numerous MMP correlations have been developed. They should be used with caution for systems similar to those used to develop the correlation. Use for other fluid systems can lead to significant errors. We discuss the advantages and disadvantages of most current methods and show that various combinations of methods can reduce uncertainty.
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