Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Abstract

Condensate reservoirs are the most challenging hydrocarbon reservoirs in the world. The behavior of condensate gas reservoirs regarding pressure and temperature variation is unique. Adjusting fluid flow rate through wellhead chokes of condensate gas wells is critical and challenging for reservoir management. Predicting this vital parameter is a big step for the development of condensate gas fields. In this study, a novel machine learning approach is developed to predict gas flow rate (Qg) from six input variables: temperature (T); upstream pressure (Pu); downstream pressure (Pd); gas gravity (γg); choke diameter (D64) and gas–liquid ratio (GLR). Due to the absence of accurate recombination methods for determining Qg, machine learning methods offer a functional alternative approach. Four hybrid machine learning (HML) algorithms are developed by integrating multiple extreme learning machine (MELM) and least squares support vector machine (LSSVM) with two optimization algorithms, the genetic algorithm (GA) and the particle swarm optimizer (PSO). The evaluation conducted on prediction performance and accuracy of the four HML models developed indicates that the MELM-PSO model has the highest Qg prediction accuracy achieving a root mean squared error (RMSE) of 2.8639 Mscf/d and a coefficient of determination (R2) of 0.9778 for a dataset of 1009 data records compiled from gas-condensate fields around Iran. Comparison of the prediction performance of the HML models developed with those of the previous empirical equations and artificial intelligence models reveals that the novel MELM-PSO model presents superior prediction efficiency and higher computational accuracy. Moreover, the Spearman correlation coefficient analysis performed demonstrates that D64 and GLR are the most influential variables in the gas flow rate for the large dataset evaluated in this study.