原油高温结垢速率测定和预测模型研究进展  被引量:4

Research Advances in Measurement and Prediction Models of Crude Oil Fouling Rate Under High Temperature

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作  者:袁宗明[1] 王勇[1] 谢英[1] 王大鹏[2] 

机构地区:[1]"油气藏地质及开发工程"国家重点实验室.西南石油大学,四川成都610500 [2]中国石油塔里木油田公司塔北勘探开发项目经理部,新疆库尔勒841000

出  处:《西南石油大学学报(自然科学版)》2016年第1期170-180,共11页Journal of Southwest Petroleum University(Science & Technology Edition)

摘  要:综述了原油高温结垢的室内测定和预测模型的研究现状,重点回顾分析了预测模型的发展历程。室内实验和模型预测是研究原油高温结垢速率的重要方法。常用室内结垢实验装置分为搅拌式和回路式,通过实验可快速获取大量有效数据,但是实验数据往往存在缺陷,无法直接应用指导现场换热器防垢。结垢预测模型需要反映原油高温结垢的化学反应本质,体现原油物性、温度、流速等重要因素以及不同过程对结垢速率的影响。目前结垢预测模型,包括已经取得巨大成果的临界模型,大多属于半经验公式,其精度和适用性始终有限。更精确的模型必须基于对结垢机理的进一步研究。由于能够成功描述非线性系统特征,人工神经网络模型也将是另一种十分有前景的预测技术。A comprehensive review in aspect of laboratory measurement and prediction models is conducted in this paper, and more focus is put on the analysis and development of prediction models. Both experiments and prediction models play significant role in determining crude oil fouling rate under high temperature. As a quick access to vast amounts of valid data, laboratory experiments, either batch stirred or recirculation test apparatus cannot directly guide fouling prevention in production. Proper models must reflect the chemical nature of fouling, the significance of influencing factors such as physical properties, temperature, flow rate and other different process. Although the threshold models have gained great achievements in field, which are semi-empirical formula, the precision and applicability of most prediction models at present are limited. More accurate models must be based on further studies on fouling mechanism. Meanwhile, according to its successful application to description of the characters of nonlinear systems, the ANN (artificial neural network) will be a new promising prediction skill.

关 键 词:原油结垢 结垢速率 实验 预测模型 人工神经网络 

分 类 号:TE624.2[石油与天然气工程—油气加工工程]

 

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