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Osamah A Alomair and Maqsood Iqbal

Osamah A Alomair and Maqsood Iqbal

Kuwait University, Kuwait

Title: CO2 minimum miscibility pressure (MMP) correlation predicts slim tube experimental data very precisely

Biography

Maqsood Iqbal is a Senior Reservoir Engineer with 20 years of diversified experience; skills encompass Reservoir Engineering, enhancedoil recovery (/IOR), Production Operations/Optimization and Petroleum Economics. Exclusive aptitudes; Well Test Analysis (WTA), Decline Curve Analysis (DCA),Material Balance Calculations, Temperature Logs Interpretations, Pressure-Volume-Temperature (PVT) studies, Non-Parametric Regression and Artificial Neural Networks. He holds M.Sc. Petroleum Engineering degree from Kuwait University.

Abstract

Miscible gas injection is one of the effective enhanced oil recovery (EOR) methods. MMP is the most important parameter to successfully design CO2 flooding, local displacement efficiency from gas injection is also highly MMP dependent, which is traditionally measured through time consuming, expensive and cumbersome experiments. A new CO2 MMP correlation based on multiple-linear-regression modeling technique has been developed to more accurately estimate the CO2 MMP for a wide range of live and heavy crude oils. The proposed CO2 MMP correlation is originated from CO2 MMP data in addition to database from the worldwide published literature that covers 33 pure CO2 MMP data for various live and dead oil samples. The proposed model trained by exploiting 66% (22 data points) of the data bank. This correlation is expressed as a function of temperature, mole fractions of in situ reservoir CO2 along with two of its hydrocarbon components (C1 and C3). A set of experimental data pool from the literature is collected to evaluate and compare the results of the developed correlation with pre-existing correlations through statistical and graphical error analyses. A statistical comparison is performed for both training data set (22 data points) as well as testing data set (11 data points). It is found that the proposed CO2 MMP correlation provides the best reproduction of MMP data with a percentage average absolute error of 11.46% and 11.92% for training and testing categories respectively. Further the correlation coefficient for the proposed correlation for training and testing data are 0.957 and 0.929, respectively. Finally, by employing the relevancy factor, it is found that the after temperature both light and intermediate (C1 and C3) components of crude oil have the most significant impact on the CO2 MMP estimation.