Comparison of Minimum Miscibility Pressures Determined from Gas-Oil Interfacial Tension Measurements with Equation of State Calculations

Abstract

Abstract Miscible gas injection has been widely used as the most popular enhanced oil recovery process for light oil reservoirs. For technical and economic success of miscible gas injection projects, an accurate laboratory measurement of minimum miscibility pressure at reservoir temperature is essential. In addition to the existing laboratory techniques of measuring MMP namely slim-tube and rising bubble, recently a new technique of measurement called vanishing interfacial tension (VIT) has been developed. This new technique enables rapid and cost-effective determination of MMP. Hence, the objective of this study is to compare the minimum miscibility pressures determined from gas-oil interfacial tension measurements with those obtained from phase behavior calculations based on equations of state (EOS). For this purpose, two commercial crude oils namely Rainbow Keg River (RKR) and Terra Nova have been used, since the PVT data necessary for EOS calculations and the experimental values of MMP were available for these two reservoir cases. Peng-Robinson (PR) equation of state within a commercial software package has been used. The effects of tuning and non-tuning the equation of state on MMP calculations have been examined. For the two reservoir cases considered in our study, tuned PR-EOS yielded significant differences between MMPs from EOS calculations and VIT measurements, while untuned PREOS yielded reasonable match with experiments. In the case of RKR crude oil, the untuned EOS predictions were consistently higher by about 3.0-5.0 MPa than the experimental MMP from the VIT technique. For Terra Nova crude oil, in three out of five cases studied, the visible MMPs from the VIT experiments reasonably matched with untuned EOS calculations. Therefore, these comparisons of VIT results with EOS predictions clearly demonstrate that this new technique of vanishing interfacial tension is quite promising and reliable.