Robust correlation to predict dew point pressure of gas condensate reservoirs

Abstract

When the bottom-hole flowing pressure in a gas condensate reservoir drops below the dew point pressure, liquid starts to build up around the well bore resulting in gas productivity decline.

For this reason it is important to be able to accurately either measure or estimate the dew point pressure. The condensate formed in the reservoir will not flow until its saturation reaches the critical saturation and in many cases it might not be entirely recovered. It order to maximize gas production and condensate recovery, the reservoir pressure must be maintained close to the dew point pressure. Several attempts have been made to predict the dew point pressure in case the gas sample becomes unavailable or measured value is unreliable. Unfortunately, most of these attempts have minor success rates and are based on limited data.

In this paper we present a robust, cheap, and easy model for predicting the dew point pressure for gas condensate reservoirs. The new model is an intelligent based model called “Gene Expression Programming” that is carried out to generate a precise and accurate correlation to estimate the dew point pressure in condensate gas reservoirs. The new model has been trained and tested using a large data bank collected for the literature. Precision of the suggested correlation has been compared to published correlations. The validity of this model has also been compared to experimental data and other published correlations.

Keywords

Dew point pressure / Gene Expression Programming / Condensate gas / Modeling

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