A COMPARATIVE ANALYSIS OF THE PREDICTION OF GAS CONDENSATE DEW POINT PRESSURE USING MACHINE LEARNING ALGORITHMS AND TRADITIONAL EMPIRICAL CORRELATIONS

Abstract

Accurate prediction of dew point pressure (Pd) is critical for managing gas condensate reservoirs, as liquid dropout near the wellbore creates condensate banking that reduces permeability and well productivity. This study proposes a data-driven approach using a Random Forest (RF) machine learning algorithm to predict Pd based on reservoir temperature and fluid composition. Utilizing a comprehensive dataset of 375 records, the model incorporates 13 predictors, including hydrocarbon fractions (C1 through C7+), non-hydrocarbons (N2, CO2, H2S), and heavy fraction properties (MC7+, γC7+). The proposed RF model exhibits highly competitive accuracy, outperforming most traditional empirical correlations by achieving a superior overall data variance capture with a Coefficient of Determination (R2) of 0.8790 and an Average Absolute Percent Relative Error (APE) of 8.17%. While the Ahmadi-Elsharkawy model shows a marginally lower APE (7.90%), the RF framework avoids complex genetic programming equations and delivers superior global consistency across the entire pressure envelope. By capturing complex, non-linear thermodynamic interactions, the RF model provides a robust, fast, and cost-effective alternative to expensive laboratory PVT tests and complex equations of state, optimizing fluid characterization and production system design.