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Prediction of Heavy-Oil Viscosities With a Simple Correlation Approach

机译:用简单的相关方法预测重油粘度

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Heavy-oil development is becoming increasingly important because of the continuous decline in conventional-oil production. For heavy-oil reservoirs, the oil viscosity usually varies dramatically during production processes such as in thermal processes. When producing heavy oil, the high viscosity is a major impediment to recovery. Oil viscosity is often correlated directly to the reserves estimate in heavy-oil formations and can determine the success or failure of a given enhanced-oil-recovery scheme. As a result, viscosity is an important parameter for perfonning numerical simulation and determining the economics of a project. In this work, a simple-to-use correlation has been developed to correlate the viscosity of heavy oil to temperature and to a simple correlating parameter that can be used for heavy-oil characterization. The reported results are the product of the analysis of heavy-oil data collected from the open literature for various heavy-oil fields around the world. The tool developed in this study can be of immense practical value for petroleum engineers, providing a method for quick assessment of the viscosity of heavy oils. In particular, petroleum and production engineers would find the proposed correlation to be user-friendly, with transparent calculations involving no complex expressions. The new proposed correlation shows consistently accurate results. This consistency could not be matched by any of the widely accepted existing correlations within the investigated range. For all conditions, the new correlation provided better results than existing correlations in the literature.
机译:由于常规石油产量的持续下降,重油的开发变得越来越重要。对于重油储层,油的粘度通常在生产过程中(例如在热过程中)急剧变化。当生产重油时,高粘度是回收的主要障碍。油粘度通常直接与重油地层的储量估算值相关,并且可以确定给定的增油采收方案的成败。因此,粘度是进行数值模拟和确定项目经济性的重要参数。在这项工作中,已经开发了一种易于使用的相关性,以将重油的粘度与温度相关联,并与可用于重油表征的简单相关性参数相关联。报告的结果是对从世界各地各种重油领域的公开文献中收集的重油数据进行分析的结果。这项研究中开发的工具对石油工程师具有巨大的实用价值,它提供了一种快速评估重油粘度的方法。特别是,石油和生产工程师会发现建议的相关性是用户友好的,并且透明的计算不涉及复杂的表达式。新提出的相关性显示出始终如一的准确结果。在研究范围内,任何一个广泛接受的现有相关性都无法匹配这种一致性。对于所有条件,新的相关性都比文献中现有的相关性提供了更好的结果。

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