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Abstract
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One of the most critical quantities for characterizing a gas condensate reservoir is dew point pressure. But, accurate determination of dew point pressure is a very challengeable task in reservoir development. Experimental measurement of dew point pressure in PVT (Pressure, Volume, Temperature) cell is often difficult, especially in the case of lean retrograde gas condensate. So, different empirical correlations and equations of state are developed by researchers to calculate this property. Empirical correlations do not have ability to reliably duplicate the temperature behavior of constant composition fluids, and equations of state have convergence problem and need to be tuned against some experimental data. In addition, these approaches are not generalizable to unseen data, and they usually memorize the data used to develop them. In this paper, we develop an intelligent model to predict dew point pressure of gas condensate reservoirs using Gaussian processes optimized by particle swarm optimization. The developed model is generalizable and can estimate unseen data with the same distribution of training data accurately. Results show that the proposed method in this paper outperforms the previous published models and correlations.
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