Prediction of crude oil viscosity curve using artificial intelligence techniques

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• 作者：M.A. Al-Marhoun ; S. Nizamuddin ; ^{nizamupm@kfupm.edu.sa} ; ^{nizamupm@gmail.com} ; A.A. Abdul Raheem ; S. Shujath Ali ; A.A. Muhammadain
• 关键词：viscosity ; bubble point ; Functional Networks ; Support Vector Machine
• 刊名：Journal of Petroleum Science and Engineering
• 出版年：2012
• 期刊代码：33_09204105
• 类别：pw
• 出版时间：May, 2012
• 卷：86-87
• 期：Complete
• 页码：111-117
• 文件大小：842 K

摘要

Viscosity of crude oil is an important physical property that controls and influences the flow of oil through rock pores and eventually dictating oil recovery. Prediction of crude oil viscosity is one of the major challenges faced by petroleum engineers in production planning to optimize reservoir production and maximize ultimate recovery.

This paper presents prediction of the complete viscosity curve as a function of pressure using artificial intelligence (AI) techniques. The viscosity curve predicted using artificial intelligence techniques derived from gas compositions of Canadian oil fields closely replicated the experimental viscosity curve above and below bubble point pressure when compared with correlations of its class. Functional Networks with Forward Selection (FNFS) outperformed all the AI techniques followed by Support Vector Machine (SVM).