Development of an artificial neural network model for predicting the dew point pressure of retrograde gas condensate

Abstract

Optimal management and development plans for gas condensate reservoirs require authentic prediction for the dew point pressure (DPP). Traditionally, DPP is experimentally measured through a constant mass expansion test (CME), however, this test is relatively prohibitive and time-consuming. Equations of state and empirical correlations are employed in determining the (DPP), but due to the lack of generalized and inaccurate predictive paradigms, it is an incentive for researchers to establish new rigorous predictive models with wider applicability ranges and higher accuracy. This study tries to develop a new intelligent predictive model and to build a simple program based on 453 gas condensate samples that cover a wide range of chemical composition, reservoir temperatures, and fluid characteristics. The artificial neural network is conducted as an intelligent tool owing to its high capability and flexibility in defining the data pattern. A detailed comparison between widely used empirical correlations, PR-EOS, SRK- EOS, and the proposed new ANN model is provided in this study. Statistical and graphical analysis depicted the outstanding performance of the new model with R2 = 95.3%, ARE = 0.0069% and AARE = 5.03%.