Toward a Reliable Model for the Prediction of CO2-Crude Oil MMP Using an Intelligent Approach
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Multiple contact miscible floods such as CO2 injection has been considered as an attractive enhanced oil recovery technique especially for conventional reservoirs. A key parameter in design of CO2 miscible floods is minimum miscibility pressure (MMP), which is normally determined through expensive and time consuming laboratory experiments. Therefore, developing a quick and reliable model for prediction of MMP is inevitable. In this communication, a new reliable model on the basis of feed forward artificial neural network is presented. The input parameters of the developed model are reservoir temperature, reservoirs oil composition, and injected gas composition. Statistical and graphical error analyses have been employed to compare the developed model with the available models for the prediction of MMP. The results indicated that the developed model is more reliable and accurate over existing models, in a wide range of thermodynamic and process conditions. Finally, the leverage approach, in which the statistical hat matrix, Williams plot, and the residuals of the model results leads to identification of the likely outlier has been performed. The results showed that only two experimental MMP data points located out of the applicability domain of the proposed model and therfore, the developed model was found could be reliable enough for the prediction of CO2- reservoir oil MMP.
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Hemmati Sarapardeh, Abdolhossein and Ghazanfari, Mohammad Hossein and Ayatollahi, Shahab and Masihi, Mohsen,1393,Toward a Reliable Model for the Prediction of CO2-Crude Oil MMP Using an Intelligent Approach,The Second National Iranian Petroleum Conference,Kerman,https://civilica.com/doc/309376
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Shokir, E.M.E.-M., CO< sub> 2</sub>-oil minimum miscibility pressure model for ...</sub> streams. Journal of Petroleum Science and Engineering, 2007. 58(1): ...Holm, L. and V. Josendal, Mechanisms of oil displacement by ...Rathmell, J., F. Stalkup, and R. Hassinger. A laboratory investigation ...Alston, R., G. Kokolis, and C. James, CO2 minimum miscibility ...Sebastian, H., R. Wenger, and T. Rener, Correlation of minimum ...Eakin, B. and F. Mitch. Measurement and correlation of miscibility ...Harmon, R. and R. Grigg, Vapor-Density Measuremet for Estimating Minimum ...Emera, M.K. and H.K. Sarma, Use of genetic algorithm to ...Jacobson, H., Acid gases and their contribution to miscibility. Journal ...Graue, D. and E. Zana, Study of a possible CO2 ...Metcalfe, R.S., Effects of impurities on minimum miscibility pressures and ...Thakur, G., C. Lin, and Y. Patel. CO2 Minitest, Little ...Gharbi, R. and A. Elsharkawy, Neural network model for estimating ...Dong, M., et al., A comparison of CO= sub> 2</subz ...Bon, J., M. Emera, and H. Sarma. An Experimental Study ...Zabihi, R., et al., Artificial neural network for permeability damage ...Kaydani, H., A. Mohebbi, and A. Baghaie, Permeability prediction based ...Yellig, W. and R. Metcalfe, Determination and Prediction of CO2 ...Hemmati -Sarapardeh, _ et al., Asphaltene precipitation due to natural ...