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Title: Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction

Abstract

Not provided.

Authors:
;
Publication Date:
Research Org.:
West Virginia Univ., Morgantown, WV (United States)
Sponsoring Org.:
USDOE Office of Policy and International Affairs (PO)
OSTI Identifier:
1533789
DOE Contract Number:  
PI0000017
Resource Type:
Journal Article
Journal Name:
Fuel
Additional Journal Information:
Journal Volume: 184; Journal Issue: C; Journal ID: ISSN 0016-2361
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
Energy & Fuels; Engineering

Citation Formats

Zhong, Zhi, and Carr, Timothy R. Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction. United States: N. p., 2016. Web. doi:10.1016/j.fuel.2016.07.030.
Zhong, Zhi, & Carr, Timothy R. Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction. United States. doi:10.1016/j.fuel.2016.07.030.
Zhong, Zhi, and Carr, Timothy R. Tue . "Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction". United States. doi:10.1016/j.fuel.2016.07.030.
@article{osti_1533789,
title = {Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction},
author = {Zhong, Zhi and Carr, Timothy R.},
abstractNote = {Not provided.},
doi = {10.1016/j.fuel.2016.07.030},
journal = {Fuel},
issn = {0016-2361},
number = C,
volume = 184,
place = {United States},
year = {2016},
month = {11}
}