Minimum Miscibility Pressure Determination: Modified Multiple Mixing Cell Method
- Tadesse Weldu Teklu (Colorado School of Mines) | Shawket G. Ghedan (Computer Modeling Group) | Ramona M. Graves (Colorado School of Mines) | Xiaolong Yin (Colorado School of Mines)
- Document ID
- Society of Petroleum Engineers
- SPE EOR Conference at Oil and Gas West Asia, 16-18 April, Muscat, Oman
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.2 Reservoir Fluid Dynamics, 5.2.2 Fluid Modeling, Equations of State, 5.7.2 Recovery Factors, 5.5 Reservoir Simulation, 5.2.1 Phase Behavior and PVT Measurements, 5.4.3 Gas Cycling, 5.4.2 Gas Injection Methods, 5.3.2 Multiphase Flow, 4.6 Natural Gas, 1.6.9 Coring, Fishing
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Hydrocarbon and non-hydrocarbon gas injection are among the most effective methods to enhance oil recovery. A key design parameter in any gas injection project is the minimum miscibility pressure (MMP). MMP can be measured in the laboratory with slim tube test, rising bubble apparatus and vanishing-interfacial-tension. Although laboratory measurements are more reliable, their high cost and longer period of time to get a few data sets made them less advantageous. There are numerous analytical, numerical, and empirical approaches devised to determine the MMP through studying thermodynamic behavior of reservoir and injection fluids in a given reservoir condition. Some numerical approaches need additional fluid and rock interaction properties such as relative permeability and capillary pressure.
Conceptually, miscibility between injected gas and reservoir oil can occur through three multi-contact mechanisms, namely condensing drive mechanism (CDM), vaporizing drive mechanism (VDM) and both condensing and vaporizing (CV) drive mechanism. Some models such as single-cell algorithm usually give overestimated MMP values since the model is based on the assumption that the drive mechanism is dictated only by CDM or VDM. In most cases, as we will see from the case studies of this research and reported by others as well, miscibility develops through both condensing and vaporizing (CV) driving mechanisms. Multiple mixing cell approach is a very effective way to handle CV drive mechanism. In this study, we modify the algorithm proposed by Ahmadi and Johns 2008, by including additional checking criterion. In spite of increased computational time overhead, our added checking criterion is found to be very useful to crosscheck the validity of the determined MMP, hence improve the reliability.
Minimum miscibility pressure (MMP) is the pressure at which local miscibility is achieved in a gas injection system. MMP can be measured in the laboratory with slim tube test (Yelling and Metcalfe 1980), rising bubble apparatus (Christiansen and Haines 1987) and the method of vanishing-interfacial-tension (Rao 1997). Laboratory MMP measurements are, however, time consuming and very expensive. The reliability of slim tube experimental MMP estimation is subjected to the influence of the experimental parameters of the tubing length and diameter, and the gas injection rate, in addition to the influence of the multiphase flow parameters such as relative permeability. Further, the rising bubble and vanishing interfacial tension do not completely capture the multi-contact mechanisms (CDM, VDM, and CV).
Many empirical correlations for MMP have been developed by fitting experimental data within the range of its reservoir conditions, reservoir fluid and injection fluid properties. These easy to use and simple formula correlations serve as a tool for quick MMP estimation. To improve the accuracy of these correlations, advanced computational methods such as Genetic Algorithms can be applied to minimize its misfit (Emera and Sarma 2005). However, since thermodynamic properties are less predictable near critical region, then a slight deviation of the reservoir condition, fluid properties or composition may lead to a large deviation of estimated MMPs from their true values. Hence, one should be very cautious when using empirical correlations to estimate MMP.
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