A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

Dew point pressure (DPP) is one of the most important parameters to characterize gas condensate reservoirs. Experimental determination of DPP in a window pressure-volume-temperature cell is often difficult especially in case of lean retrograde gas condensate. Therefore, searching for fast and robust algorithms for determination of DPP is usually needed. This paper presents a new approach based on artificial neural network (ANN) to determine DPP. The back-propagation learning algorithms were used in the network as the best approach. Then equations for DPP prediction by using weights of the network were generated. With the obtained correlation, the user may use such results without a running the ANN software. Consequently, this new model is compared with results obtained using other conventional models to make evaluation among different techniques. The results show that the neural model can be applied effectively and afford high accuracy and dependability for DPP forecasting for the wide range of gas properties and reservoir temperatures.

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