DOI: 10.5176/2251-189X_SEES18.48

Authors: Osamah Alomair, Maqsood Iqbal, Raghad Abulhasan, Shymaa Al-Qattan, and Layal Al-Otaibi

Abstract: A key parameter in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from gas injection is highly dependent on the MMP. In case of CO2 and enriched hydrocarbon (methane with propane and butane added) flooding, a considerable published data is available; therefore, the screening criteria for a successful miscible flooding can easily be envisaged. However, the use of nitrogen as a miscible solvent has not been as extensively studied and the miscibility data are much sparser. Therefore, a new N2 MMP correlation based on MLR modelling technique has been successfully developed to more accurately estimate the N2 MMP for a wide range of live and heavy crude oils. The newly developed N2 MMP correlation is originated from N2 MMP experimental data in addition to database extracted from the worldwide published literature that covers about 30 pure N2 MMP data points for various live and dead oil samples. The proposed model is trained by exploiting 67{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} (20 data points) of the data bank. The empirical correlation takes into account the effects of in-situ oil composition and reservoir temperature. Further, to investigate the authenticity of the correlation, a detailed statistical comparison has been conducted with the most commonly used pure N2 MMP correlations from the published literature. A statistical comparison is performed for both training data set (20 data points) as well as testing data set (10 data points). It is found that the proposed N2 MMP correlation provides the best reproduction of MMP data with a percentage average absolute error of 3.33{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} and 2.23{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} for training and testing categories respectively.

Keywords: EOR, MMP, N2, Regression and Miscibility.


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