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Title: Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction

Authors:
;
Publication Date:
Type:
Publisher's Accepted Manuscript
Journal Name:
Fuel
Additional Journal Information:
Journal Volume: 184; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-09-10 05:52:28; Journal ID: ISSN 0016-2361
Publisher:
Elsevier
Sponsoring Org:
USDOE
Country of Publication:
United Kingdom
Language:
English
OSTI Identifier:
1399037

Zhong, Zhi, and Carr, Timothy R. Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction. United Kingdom: N. p., 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 CO 2 – Reservoir oil minimum miscibility pressure prediction. United Kingdom. doi:10.1016/j.fuel.2016.07.030.
Zhong, Zhi, and Carr, Timothy R. 2016. "Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction". United Kingdom. doi:10.1016/j.fuel.2016.07.030.
@article{osti_1399037,
title = {Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction},
author = {Zhong, Zhi and Carr, Timothy R.},
abstractNote = {},
doi = {10.1016/j.fuel.2016.07.030},
journal = {Fuel},
number = C,
volume = 184,
place = {United Kingdom},
year = {2016},
month = {11}
}