文章基本信息

标题：Data analytics for oil sands subcool prediction — a comparative study of machine learning algorithms
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作者：Chaoqun Li ; Nabil Magbool Jan ; Biao Huang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：886-891
DOI：10.1016/j.ifacol.2018.09.234
语种：English
出版社：Elsevier
摘要：AbstractSteam Assisted Gravity Drainage (SAGD) is an efficient and widely used technology to extract heavy oil from a reservoir. The accurate prediction of subcool plays a critical role in determining the economic performance of SAGD operations since it influences oil production and operational safety. This work focuses on developing a subcool model based on industrial datasets using deep learning and several other widely-used machine learning methods. Furthermore, this work compares and discusses the out-of-sample performance of different machine learning algorithms using industrial datasets. In addition, we also show that care has to be taken when using machine learning algorithms to solve engineering problems. Data quality and a priori process knowledge play a role in their performance.
关键词：Keywordsdata analyticsdeep learningprocess applicationmachine learningSAGDsubcool