Abstract
Miscible gas injection has been considered one of the most important enhanced oil recovery techniques. Minimum miscibility pressure (MMP) is a key parameter in the design of an efficient miscible gas injection project. This parameter is usually determined using a slimtube apparatus in the laboratory. However, many attempts have been made to introduce MMP predicting correlations. In this study an adaptive neuro-fuzzy inference system (ANFIS)–based correlation has been developed to estimate the MMP values. In this model, the MMP of reservoir fluid is correlated with 27 variables containing concentrations of different components in reservoir oil and injecting gas, molecular weight and specific gravity of C7 + in reservoir oil and also reservoir temperature. This correlation can be applied to predict the effect of each individual parameter on the MMP values.
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