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

Zhong, Zhi; Carr, Timothy R.

doi:10.1016/j.fuel.2016.07.030

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

Journal Article · Thu Jul 21 00:00:00 EDT 2016 · Fuel

DOI:https://doi.org/10.1016/j.fuel.2016.07.030· OSTI ID:1533789

Zhong, Zhi ^[1]; Carr, Timothy R. ^[2]

West Virginia University, Morgantown, WV (United States); DOE/OSTI
West Virginia University, Morgantown, WV (United States)

Carbon dioxide (CO₂) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO₂-EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO₂ injection process. Traditional empirical methods to test the CO₂-oil MMP are time consuming and expensive; derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. Here in this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO₂ injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature; (2) average critical temperature; (3) molecular weight of pentane plus (C₅₊) fraction of crude oil, and; (4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work.

View Accepted Manuscript (DOE)

Research Organization:: West Virginia University, Morgantown, WV (United States)

Sponsoring Organization:: USDOE; USDOE Office of Policy and International Affairs (PO)

Grant/Contract Number:: PI0000017

OSTI ID:: 1533789

Alternate ID(s):: OSTI ID: 1399037

Journal Information:: Fuel, Journal Name: Fuel Journal Issue: C Vol. 184; ISSN 0016-2361

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (61)

Phase relations of miscible displacement in oil recovery Hutchinson, C. A.; Braun, Philip H. AIChE Journal, Vol. 7, Issue 1 https://doi.org/10.1002/aic.690070117	journal	March 1961
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/BF00994018	journal	September 1995
Comparative studies of metamodelling techniques under multiple modelling criteria Jin, R.; Chen, W.; Simpson, T. W. Structural and Multidisciplinary Optimization, Vol. 23, Issue 1 https://doi.org/10.1007/s00158-001-0160-4	journal	December 2001
A study on the minimum miscibility pressure for miscible flooding systems Zuo, You-xiang; Chu, Ji-zheng; Ke, Shui-lin Journal of Petroleum Science and Engineering, Vol. 8, Issue 4 https://doi.org/10.1016/0920-4105(93)90008-3	journal	January 1993
An improved model calculating CO2 solubility in pure water and aqueous NaCl solutions from 273 to 533 K and from 0 to 2000 bar Duan, Zhenhao; Sun, Rui Chemical Geology, Vol. 193, Issue 3-4 https://doi.org/10.1016/S0009-2541(02)00263-2	journal	February 2003
A new technique of vanishing interfacial tension for miscibility determination Rao, Dandina N. Fluid Phase Equilibria, Vol. 139, Issue 1-2 https://doi.org/10.1016/S0378-3812(97)00180-5	journal	December 1997
A comparison of CO2 minimum miscibility pressure determinations for Weyburn crude oil Dong, Mingzhe; Huang, Sam; Dyer, Steve B. Journal of Petroleum Science and Engineering, Vol. 31, Issue 1 https://doi.org/10.1016/S0920-4105(01)00135-8	journal	October 2001
A crude oil data bank containing more than 5000 PVT and gas injection data Jaubert, Jean-Noël; Avaullee, Laurent; Souvay, Jean-François Journal of Petroleum Science and Engineering, Vol. 34, Issue 1-4 https://doi.org/10.1016/S0920-4105(02)00153-5	journal	June 2002
Application of the new vanishing interfacial tension technique to evaluate miscibility conditions for the Terra Nova Offshore Project Rao, Dandina N.; Lee, J. I. Journal of Petroleum Science and Engineering, Vol. 35, Issue 3-4 https://doi.org/10.1016/S0920-4105(02)00246-2	journal	August 2002
Development of an artificial neural network model for predicting minimum miscibility pressure in CO2 flooding Huang, Y. F.; Huang, G. H.; Dong, M. Z. Journal of Petroleum Science and Engineering, Vol. 37, Issue 1-2 https://doi.org/10.1016/S0920-4105(02)00312-1	journal	February 2003
Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study Al-Anazi, A. F.; Gates, I. D. Computers & Geosciences, Vol. 36, Issue 12 https://doi.org/10.1016/j.cageo.2010.03.022	journal	December 2010
Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin Wang, Guochang; Carr, Timothy R.; Ju, Yiwen Computers & Geosciences, Vol. 64 https://doi.org/10.1016/j.cageo.2013.12.002	journal	March 2014
Minimum miscibility pressure prediction based on a hybrid neural genetic algorithm Dehghani, S. A. Mousavi; Sefti, M. Vafaie; Ameri, A. Chemical Engineering Research and Design, Vol. 86, Issue 2 https://doi.org/10.1016/j.cherd.2007.10.011	journal	February 2008
Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model Hong, Wei-Chiang Energy Conversion and Management, Vol. 50, Issue 1 https://doi.org/10.1016/j.enconman.2008.08.031	journal	January 2009
A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs Al-Anazi, A.; Gates, I. D. Engineering Geology, Vol. 114, Issue 3-4 https://doi.org/10.1016/j.enggeo.2010.05.005	journal	August 2010
An analysis of the vanishing interfacial tension technique for determination of minimum miscibility pressure Orr, Franklin M.; Jessen, Kristian Fluid Phase Equilibria, Vol. 255, Issue 2 https://doi.org/10.1016/j.fluid.2007.04.002	journal	July 2007
Fuzzy Modeling and Experimental Investigation of Minimum Miscible Pressure in Gas Injection Process Ahmadi, Mohammad-Ali; Ebadi, Mohammad Fluid Phase Equilibria, Vol. 378 https://doi.org/10.1016/j.fluid.2014.06.022	journal	September 2014
Prediction of minimum miscibility pressure in oil reservoirs using a modified SAFT equation of state Fazlali, Alireza; Nikookar, Mohammad; Agha-Aminiha, Alireza Fuel, Vol. 108 https://doi.org/10.1016/j.fuel.2012.12.091	journal	June 2013
Intelligent model for prediction of CO2 – Reservoir oil minimum miscibility pressure Shokrollahi, Amin; Arabloo, Milad; Gharagheizi, Farhad Fuel, Vol. 112 https://doi.org/10.1016/j.fuel.2013.04.036	journal	October 2013
Application of hybrid neural particle swarm optimization algorithm for prediction of MMP Sayyad, Hossein; Manshad, Abbas Khaksar; Rostami, Habib Fuel, Vol. 116 https://doi.org/10.1016/j.fuel.2013.08.076	journal	January 2014
The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process Chen, Guangying; Fu, Kaiyun; Liang, Zhiwu Fuel, Vol. 126 https://doi.org/10.1016/j.fuel.2014.02.034	journal	June 2014
Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO 2 flooding Kamari, Arash; Arabloo, Milad; Shokrollahi, Amin Fuel, Vol. 153 https://doi.org/10.1016/j.fuel.2015.02.087	journal	August 2015
Prediction of nitrogen diluted CO 2 minimum miscibility pressure for EOR and storage in depleted oil reservoirs Wang, Jinjie; Dong, Mingzhe; Li, Yajun Fuel, Vol. 162 https://doi.org/10.1016/j.fuel.2015.08.075	journal	December 2015
Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams Bian, Xiao-Qiang; Han, Bing; Du, Zhi-Min Fuel, Vol. 182 https://doi.org/10.1016/j.fuel.2016.05.124	journal	October 2016
Implementing Radial Basis Function Networks for modeling CO2-reservoir oil minimum miscibility pressure Tatar, Afshin; Shokrollahi, Amin; Mesbah, Mohammad Journal of Natural Gas Science and Engineering, Vol. 15 https://doi.org/10.1016/j.jngse.2013.09.008	journal	November 2013
Use of genetic algorithm to estimate CO2–oil minimum miscibility pressure—a key parameter in design of CO2 miscible flood Emera, Mohammed K.; Sarma, Hemanta K. Journal of Petroleum Science and Engineering, Vol. 46, Issue 1-2 https://doi.org/10.1016/j.petrol.2004.10.001	journal	February 2005
CO2–oil minimum miscibility pressure model for impure and pure CO2 streams Shokir, Eissa M. El-M. Journal of Petroleum Science and Engineering, Vol. 58, Issue 1-2 https://doi.org/10.1016/j.petrol.2006.12.001	journal	August 2007
First-Contact-Miscible and Multicontact-Miscible Gas Injection within a Channeling Heterogeneity System Al-Wahaibi, Yahya M. Energy & Fuels, Vol. 24, Issue 3 https://doi.org/10.1021/ef901277v	journal	February 2010
Carbon Dioxide Solubility in Water Dodds, W. S.; Stutzman, L. F.; Sollami, B. J. Industrial & Engineering Chemistry Chemical & Engineering Data Series, Vol. 1, Issue 1 https://doi.org/10.1021/i460001a018	journal	January 1956
An Improved CO ₂ –Oil Minimum Miscibility Pressure Correlation for Live and Dead Crude Oils Li, Huazhou; Qin, Jishun; Yang, Daoyong Industrial & Engineering Chemistry Research, Vol. 51, Issue 8 https://doi.org/10.1021/ie202339g	journal	February 2012
Experimental Determination of Interfacial Tension and Miscibility of the CO₂–Crude Oil System; Temperature, Pressure, and Composition Effects Hemmati-Sarapardeh, Abdolhossein; Ayatollahi, Shahab; Ghazanfari, Mohammad-Hossein Journal of Chemical & Engineering Data, Vol. 59, Issue 1 https://doi.org/10.1021/je400811h	journal	December 2013
A Tutorial on Support Vector Machines for Pattern Recognition Burges, Christopher J. C. Data Mining and Knowledge Discovery, Vol. 2, Issue 2, p. 121-167 https://doi.org/10.1023/A:1009715923555	journal	January 1998
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
What is a support vector machine? Noble, William S. Nature Biotechnology, Vol. 24, Issue 12 https://doi.org/10.1038/nbt1206-1565	journal	December 2006
Oil-CO₂MMP Determination in Competition of Neural Network, Support Vector Regression, and Committee Machine Asoodeh, Mojtaba; Gholami, Amin; Bagheripour, Parisa Journal of Dispersion Science and Technology, Vol. 35, Issue 4 https://doi.org/10.1080/01932691.2013.803255	journal	March 2014
Robust linear programming discrimination of two linearly inseparable sets Bennett, Kristin P.; Mangasarian, O. L. Optimization Methods and Software, Vol. 1, Issue 1 https://doi.org/10.1080/10556789208805504	journal	January 1992
Importance of input data normalization for the application of neural networks to complex industrial problems Sola, J.; Sevilla, J. IEEE Transactions on Nuclear Science, Vol. 44, Issue 3 https://doi.org/10.1109/23.589532	journal	June 1997
Precise model for estimating CO2—oil minimum miscibility pressure Shokir, Eissa M. El-M. Petroleum Chemistry, Vol. 47, Issue 5 https://doi.org/10.1134/S0965544107050106	journal	September 2007
An Improved CO₂-Crude Oil Minimum Miscibility Pressure Correlation Zhang, Hao; Hou, Dali; Li, Kai Journal of Chemistry, Vol. 2015 https://doi.org/10.1155/2015/175940	journal	January 2015
Laboratory Investigation of Weyburn CO2 Miscible Flooding Srivastava, R. K.; Huang, S. S.; Dong, M. Journal of Canadian Petroleum Technology, Vol. 39, Issue 02 https://doi.org/10.2118/00-02-04	journal	February 2000
Carbon Dioxide Flooding Evaluation of High-Pour-Point, Paraffinic Red Wash Reservoir Oil Frimodig, James P.; Reese, Norman A.; Williams, Craig A. Society of Petroleum Engineers Journal, Vol. 23, Issue 04 https://doi.org/10.2118/10272-PA	journal	August 1983
Miscible Drive - Its Theory and Application Clark, Norman J.; Shearin, H. M.; Schultz, W. P. Journal of Petroleum Technology, Vol. 10, Issue 06 https://doi.org/10.2118/1036-G	journal	June 1958
Interpretation of Pressure-Composition Phase Diagrams for CO2/Crude-Oil Systems Orr, F. M.; Jensen, C. M. Society of Petroleum Engineers Journal, Vol. 24, Issue 05 https://doi.org/10.2118/11125-PA	journal	October 1984
CO2 Minimum Miscibility Pressure: A Correlation for Impure CO2 Streams and Live Oil Systems Alston, R. B.; Kokolis, G. P.; James, C. F. Society of Petroleum Engineers Journal, Vol. 25, Issue 02 https://doi.org/10.2118/11959-PA	journal	April 1985
Support-Vector Regression for Permeability Prediction in a Heterogeneous Reservoir: A Comparative Study Al-anazi, Ammal F.; Gates, Ian D. SPE Reservoir Evaluation & Engineering, Vol. 13, Issue 03 https://doi.org/10.2118/126339-PA	journal	June 2010
Correlation of Minimum Miscibility Pressure for Impure CO2 Streams Sebastian, H. M.; Wenger, R. S.; Renner, T. A. Journal of Petroleum Technology, Vol. 37, Issue 11 https://doi.org/10.2118/12648-PA	journal	November 1985
Generalized Minimum Miscibility Pressure Correlation (includes associated papers 15845 and 16287 ) Glaso, O. Society of Petroleum Engineers Journal, Vol. 25, Issue 06 https://doi.org/10.2118/12893-PA	journal	December 1985
Rapid Measurement of Minimum Miscibility Pressure With the Rising-Bubble Apparatus Christiansen, Richard L.; Haines, Hiemi Kim SPE Reservoir Engineering, Vol. 2, Issue 04 https://doi.org/10.2118/13114-PA	journal	November 1987
Evaluation and Design of a CO2 Miscible Flood Project-SACROC Unit, Kelly-Snyder Field Dicharry, Roy M.; Perryman, T. L.; Ronquille, J. D. Journal of Petroleum Technology, Vol. 25, Issue 11 https://doi.org/10.2118/4083-PA	journal	November 1973
Mechanisms of Oil Displacement By Carbon Dioxide Holm, L. W.; Josendal, V. A. Journal of Petroleum Technology, Vol. 26, Issue 12 https://doi.org/10.2118/4736-PA	journal	December 1974
Multiple Phase Behavior in Porous Media During CO2 or Rich-Gas Flooding Shelton, J. L.; Yarborough, L. Journal of Petroleum Technology, Vol. 29, Issue 09 https://doi.org/10.2118/5827-PA	journal	September 1977
Study of a Possible CO2 Flood in Rangely Field Graue, Dennis J.; Zana, E. T. Journal of Petroleum Technology, Vol. 33, Issue 07 https://doi.org/10.2118/7060-PA	journal	July 1981
Acid Gases And Their Contribution to Miscibility Jacobson, H. A. Journal of Canadian Petroleum Technology, Vol. 11, Issue 02 https://doi.org/10.2118/72-02-03	journal	April 1972
Determination and Prediction of CO2 Minimum Miscibility Pressures (includes associated paper 8876 ) Yellig, W. F.; Metcalfe, R. S. Journal of Petroleum Technology, Vol. 32, Issue 01 https://doi.org/10.2118/7477-PA	journal	January 1980
Simplified Method for Calculation of Minimum Miscibility Pressure or Enrichment Yuan, Hua; Johns, Russell T. SPE Journal, Vol. 10, Issue 04 https://doi.org/10.2118/77381-PA	journal	December 2005
Carbon Dioxide Flooding-fundamentals Mungan, Necmettin Journal of Canadian Petroleum Technology, Vol. 20, Issue 01 https://doi.org/10.2118/81-01-03	journal	January 1981
Multiple-Phase Generation During Carbon Dioxide Flooding Henry, Richard L.; Metcalfe, Robert S. Society of Petroleum Engineers Journal, Vol. 23, Issue 04 https://doi.org/10.2118/8812-PA	journal	August 1983
Improved MMP Correlation for CO2 Floods Using Analytical Theory Yuan, Hua; Johns, Russell T.; Egwuenu, Azubuike M. SPE Reservoir Evaluation & Engineering, Vol. 8, Issue 05 https://doi.org/10.2118/89359-PA	journal	October 2005
Effects of Impurities on Minimum Miscibility Pressures and Minimum Enrichment Levels for CO2 and Rich-Gas Displacements Metcalfe, R. S. Society of Petroleum Engineers Journal, Vol. 22, Issue 02 https://doi.org/10.2118/9230-PA	journal	April 1982
Phase Equilibria in CO2—Multicomponent Hydrocarbon Systems: Experimental Data and an Improved Prediction Technique Turek, Edward A.; Metcalfe, Robert S.; Yarborough, Lyman Society of Petroleum Engineers Journal, Vol. 24, Issue 03 https://doi.org/10.2118/9231-PA	journal	June 1984
Status of Miscible Displacement Stalkup, Fred I. Journal of Petroleum Technology, Vol. 35, Issue 04 https://doi.org/10.2118/9992-PA	journal	April 1983

Similar Records

A machine learning model for predicting the minimum miscibility pressure of CO₂ and crude oil system based on a support vector machine algorithm approach

Journal Article · Tue Dec 29 19:00:00 EST 2020 · Fuel · OSTI ID:1849150

Thermodynamic correlation for the minimum miscibility pressure of carbon dioxide flooding of petroleum reservoirs

Thesis/Dissertation · Mon Dec 31 23:00:00 EST 1984 · OSTI ID:5486280

A comprehensive techno-eco-assessment of CO₂ enhanced oil recovery projects using a machine-learning assisted workflow

Journal Article · Thu Oct 07 20:00:00 EDT 2021 · International Journal of Greenhouse Gas Control · OSTI ID:1977177

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
42 ENGINEERING
CO2 enhanced oil recovery
CO2-oil minimum miscibility pressure
mixed kernels function
particle swarm optimization algorithm
support vector regression

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

Citation Formats

References (61)

Similar Records

Related Subjects

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