期刊
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
卷 182, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.petrol.2019.106320
关键词
Asphaltene precipitation; Deposition; Scaling equation; Gas injection
资金
- National Iranian South Oil Company (NISOC)
Asphaltene precipitation and subsequent deposition on the rock surface during natural depletion and Enhanced Oil Recovery (EOR) methods are two challenging flow assurance issues, causing a sudden decline in oil production. Different precipitation mechanisms and absence of an appropriate generalized characterization parameter have questioned the predictive potential of numerous available thermodynamic models. To overcome the complexity of using available thermodynamic models, different scaling equations were developed to represent a simple yet accurate estimation of the amount of asphaltene precipitation. Nonetheless, most of the proposed correlations are not generalized and are best suited to a specific scenario such as addition of a precipitant or gas injection. In this study, several asphaltene precipitation experiments were conducted on different real oil samples to investigate the amount of precipitated asphaltene due to the compositional changes cause by injection of Methane and Water and also due to pressure variations representing natural depletion mechanism. Besides performing the experiments, the collected literature data for precipitant dilution and gas injection were used to propose a new generalized scaling equation by developing the idea suggested by Kord and Ayatollahi to model all types of asphaltene precipitation phenomena. The results indicated that the new generalized scaling equation can successfully model all types of asphaltene precipitation scenarios. The amount of precipitation can be estimated at different thermodynamic conditions for four precipitation phenomena. The results illustrated that the new generalized scaling equation can successfully model all factors triggering asphaltene precipitation at various stages of production, demonstrating its potential for use as an efficient tool to identify, assess and mitigate risk of asphaltene deposition.
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