Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas

Abstract

The development of oil and gas resources in global petroliferous basins has extended from shallow to deep reservoirs. Dew point pressure (Pd) is a vital parameter for fluid characterization and field development. Accurately and quickly obtaining Pd is crucial for the development of ultra-deep condensate gas reservoirs. The objective of this work is to predict the Pd of condensate gas by an artificial neural network (ANN) model. Ten ultra-deep condensate gas samples were analyzed using an experimental method and the Pd at reservoir temperature was obtained. A total of 113 datasets including 103 collected datasets and 10 measured datasets were adopted for ANN model training and testing. The results show that the average absolute percent relative error (AAPRE) of the developed ANN model between the measured and predicted values on the test set was 4.9589%. The predicted accuracy between the ANN model and widely used equations of state was compared. The results of statistical and graphical analysis show that the ANN model achieves the minimum prediction error. This ANN model can provide the necessary guidance for predicting the Pd for the development of different kinds of reservoirs.