Regression technique with dynamic parameter selection for phase-behavior matching

作     者：Agarwal, Rajeev K. Li, Yau-Kun Nghiem, Long 

作者机构：Computer Modelling Group 

出 版 物：《SPE Reservoir Engineering (Society of Petroleum Engineers)》 (SPE Reservoir Eng)

年 卷 期：1990年第5卷第1期

页      面：115-12016343页

核心收录：

主　　题：Gas oil 

摘      要：The major problem in phase-behavior matching with a cubic equation of state (EOS) is the selection of regression parameters. Many parameters can be selected as the best set of parameters;therefore, a dynamic parameter-solution scheme is desired to avoid tedious and time-consuming trial-and-error regression runs. This paper proposes a regression technique in which the most significant parameters are selected from a large set of parameters during the regression process. This technique reduces the regression effort considerably and alleviates the problem associated with a priori selection of regression parameters. The technique s success is demonstrated by matching experimental data for a light oil and a gas condensate.