Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach

Ekechukwu, Gerald Kelechi and Falode, O. and Orodu, O. D. (2020) Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach. J. Energy Resour. Technol, 142 (12).

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Abstract

The minimum miscibility pressure (MMP) is one of the critical parameters needed in the successful design of a miscible gas injection for enhanced oil recovery purposes. In this study, we explore the capability of using the Gaussian process machine learning (GPML) approach, for accurate prediction of this vital property in both pure and impure CO2-injection streams. We first performed a sensitivity analysis of different kernels and then a comparative analysis with other techniques. The new GPML model, when compared with previously published predictive models, including both correlations and other machine learning (ML)/intelligent models, showed superior performance with the highest correlation coefficient and the lowest error metrics.

Item Type:	Article
Uncontrolled Keywords:	minimum miscibility pressure, machine learning, Gaussian process regression, CO2 flooding, Bayesian approach, oil/gas reservoirs, petroleum engineering, underground injection and storage
Subjects:	T Technology > T Technology (General) T Technology > TP Chemical technology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	nwokealisi
Date Deposited:	31 Oct 2022 13:55
Last Modified:	31 Oct 2022 13:55
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/16399

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