Summary
Bubble-point pressure is a crucial parameter in reservoir and production engineering in the oil and gas industry, but its accurate determination through experimental methods is both costly and time-consuming. Alternative approaches, such as equations of state and empirical correlations like Al Marhoun, Dokla and Osman, Glaso, Standing, and Vazquez and Beggs, are commonly used but suffer from limitations including their inability to capture complex, non-linear relationships and adapt to new or high-dimensional data.
This study aims to address these shortcomings by developing and evaluating a range of machine learning models—including Decision Tree, Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), AdaBoosting, Gradient Boosting, Stacked Super Learner, and Multilayer Perceptron Neural Network (MLPNN)—for predicting bubble-point pressure as a function of the reservoir temperature, gas gravity, solution gas-oil ratio, and oil gravity (API).
Utilizing a comprehensive dataset derived from different published papers, a total of 776 data sets were used in this study which were divided into 80% for training and 20% for testing. The study employed performance metrics such as Average Percentage Relative Error (APRE), Absolute Average Percentage Relative Error (AAPRE), Root Mean Square Error (RMSE), and Coefficient of Determination for evaluation. The Gradient Boosting model emerged as the most effective, with an RMSE of 364.027 and an R2 of 0.924 on the test data, outperforming the existing correlations used in this study.
The results demonstrate the potential of machine learning models, particularly the Gradient Boosting model, in offering advantages such as capturing complex relationships thereby contributing to more effective reservoir management strategies.
