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Miscible injection of carbon dioxide (CO2) with ability to increase oil displacement as well as to reduce greenhouse effect has become one of the pioneering methods in Enhanced Oil Recovery (EOR). Minimum Miscibility Pressure (MMP) is known as a key indicator to ensure complete miscibility of two phases and maximum efficiency of injection process. There are various experimental and computational methods to calculate this key parameter. Experimental methods provide the most accurate and valid results. However, such methods are time consuming and expensive leading researchers to use mathematical methods. Among computational methods, empirical correlations are the most straight-forward and simple tools to precisely estimate MMP, especially for gases with impurities. Furthermore, in predicting the miscibility state of oil–gas system, phase behavior is a vital issue which should be taken into account to achieve reliable results. In this regard, equations of state have an indisputable role in predicting the phase behavior of reservoir fluids. Remarkable improvements have been introduced to elevate performance of equations of state, based on Pitzer’s acentric factor. Hereupon, this study aims to enumerate acentric factor of injected gas (impure CO2) as a correlating parameter alongside conventional parameters including reservoir temperature, oil constituents (molecular weight of C5+, ratio of volatiles to intermediates) and critical properties of injected gas (pseudo-critical pressure & temperature). Thus, in this study an effective empirical correlation is created, implementing the Group Method of Data Handling (GMDH) algorithm along with including the acentric factor of injected gas, which eventuated to precise predictions of MMP for impure CO2 injection. The GMDH is one of the most robust mathematical modeling methods for predicting physical parameters using linear equations. A comparison with well-known correlations, demonstrated at least 2% improvement in average absolute error with enumerating the acentric factor and the final error was equal to 12.89%.

The effect of injected gas composition (as minimum miscible enrichment) on minimum miscibility pressure (MMP) and respective oil recovery has been studied through numerical slim tube simulation on a wide range of gas compositions. An initial comparative study on the performances of representative equations of state for predicting MMP in slim tube simulations has proven Peng–Robinson equation of state to provide results with better agreement with experimental data. Different injection scenarios are then designed to understand the effects of varying percentages of each component (like CO2) into the injected gas mixture on MMP. MMP decreased when the concentration of CO2 and intermediate components (C2–C6) increased. It was also found that when C7+ content in the injected gas increased, recovery factor decreased significantly. Further investigations on the effect of adding benzene, toluene, and xylene as chemicals into the injected gas (in slim tube simulations) also prove that addition of these aromatics can obviously improve oil recovery and decrease MMP and toluene gives the better results in comparison with the other ones. © 2017 Curtin University of Technology and John Wiley & Sons, Ltd.

It is crucial to accurately determine the gas compressibility factor (z factor) in chemical and petroleum engineering calculations. Empirical correlations are one of the most rapid and convenient methods of determining the gas compressibility factor. Most of correlations have been developed based on data from Standing–Katz chart, where for high‐pressure conditions (P > 3,000 Psia), solely the data of pure methane was adopted and finally plotted. Therefore, the performance of these correlations would deteriorate for pressurized gas reservoirs. This study intends to use the adopted data of Iranian gas reservoirs (as the second largest source) to develop an empirical correlation concerning with pressurized gas reservoirs. To this end, the correlation was presented based on corresponding states model through the robust group method of data handling algorithm. In comparison with foregone studies, results suggested improvements in prediction of gas compressibility factor with the proposed correlation by up to 9% of average absolute percentage relative error, yielding the error of 1.46% against real data.

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