Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction-Reference-Cited by-同舟云学术

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

Published:2016-11 Issue: Volume:184 Page:590-603
ISSN:0016-2361
Container-title:Fuel
language:en
Short-container-title:Fuel

Author:

Zhong Zhi,Carr Timothy R.

Funder

US-China Clean Energy Research Center

Advanced Coal Technology Consortium

National Energy Technology Laboratory of the US Department of Energy

Publisher

Elsevier BV

Subject

Organic Chemistry,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Reference108 articles.

1. Nontechnical guide to petroleum geology, exploration, drilling & production;Hyne,2012

2. CO2-EOR set for growth as new CO2 supplies emerge;Kuuskraa;Oil Gas J,2014

3. Carbon dioxide enhanced oil recovery-untapped domestic energy supply and long term carbon storage solution;NETL,2010

4. Improved MMP correlations for CO2 floods using analytical gas flooding theory;Yuan,2004

5. Simplified method for calculation of minimum miscibility pressure or enrichment;Yuan;SPE J.,2005

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