Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach
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Abstract
Description
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.
Keywords
T Technology (General), TP Chemical technology