Rapid method for the estimation of dew point pressures in gas condensate reservoirs

Abstract The production of condensate, in addition to gas can improve the recovery factor of gas condensate reservoirs, as well as increase the economic feasibility of the reservoir. Dew point pressure (DPP) is regarded as one of the vital parameters for characterizing a gas condensate reservoir. The accurate estimation of DPP is however still a major challenge for reservoir engineers. In this study, a consistent, accurate, and simple-to-use model is proposed for the prediction of DPP in gas condensate reservoirs using a reliable soft-computing approach known as gene expression programming (GEP). The computational approach utilizes a comprehensive dataset of DPP, as well as properties of C7+, reservoir temperature, and hydrocarbon and non-hydrocarbon reservoir fluid compositions. The model proposed is compared to three well-known empirical correlations. The proposed model produces an average absolute relative deviation of approximately 7.88% and is clearly superior to previously published methods for the prediction of dew point pressure in gas condensate reservoirs.

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