Several methods to determine the MMP can be introduced for the forecast. Slim tube test is one of the widely accepted methods for the use of actual crude oil, but it is high cost and time consuming. The accuracy of slim tube simulation method greatly depends on the accuracy of compositional model matched by the PVT test data. Analytical method is very fast, but it is difficult to find the unique and correct set of key tie lines in the displacements is very difficult. This paper presents a simple and accurate empirical correlation for MMP prediction of miscible flooding, which is faster and less cumbersome than slim tube test and slim tube simulation. Unlike previous empirical correlation methods, our new correlation considers the effect of both reservoir temperature, oil composition and gas contamination, as well as the influencing degree. For pure CO2, except reservoir temperature, mole fractions of the (C1+N2), (C2−C4), and (C5−C6), molecular weight of C7+of the crude oil are taken as the influencing factors for the MMP prediction. It is found that the relationship of MMP and these parameters is linearly dependent, thus, regress function based on least square method is used to fit a new correlation. On this basis, mole fractions of CH4, N2, H2S, and intermediate components (C2−C6) in the injection gases were separately selected to predict the MMP differences caused by the contaminated gas. Nlinfit function of nonlinear fitting was proposed due to the complex effect of reservoir temperature and volatile components of the oil on the mixing of oil and injection gases. Our approach is more accurate and robust than most of previous empirical correlations which lack specific consideration of the key factors. According our new empirical correlation, the MMP of miscible flooding for pure and impure CO2 can be calculated in minutes using by using a simple Excel spreadsheet with the input of just very basic data. Our approach supplies a more accurate method for the fast screening of potential oilfield to implement CO2 miscible flooding.

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