A comparative analysis of bubble point pressure prediction using advanced machine learning algorithms and classical correlations

The need for fluid properties or PVT (Pressure-Volume-Temperature) properties, is part of the entire Exploration and Production (E&P) lifecycle from exploration to mature asset management to the typical later life events such as, Improved Oil Recovery (IOR). As the projects mature, the need for such data and its integration for various discipline-specific workflows and its interpretation in the light of reservoir performance varies. Among all the key PVT properties, bubble point pressure is probably the most important parameter. Bubble point pressure is important because it is the point at which constant composition and variable composition portions of the depletion paths merge. Geometrically, bubble point pressure appears to be a discontinuity. In addition, it dictates the existence (or not) of the incipient phase (i.e., gas phase) leading to the changes in the flow characteristics both in porous media and as well as within the wellbore and the facilities. Furthermore, it is also a good indicative of a possible gas cap when the reservoir is at saturation (reservoir pressure is equal to the bubble point pressure) or near-saturated. Among the highlighted uses, there are many more used such as the determination of the elements of miscibility, gas lift design, etc. Therefore, it is very important to estimate the bubble point pressure accurately. In this study, tree-based advanced machine learning algorithm including XGBoost, LightGBM, and random forest regressor, and multi-layer perceptron (neural network) regressor are implemented to predict bubble point pressure (P-bp). A novel super learner model which is also known as stacking ensemble is used to enhance base machine learning model performance on predicting bubble point pressure. Three datasets with different predictors are prepared to study machine learning algorithms' performance for three situations: only compositional data are available; only bulk properties (Gas-Oil-Ratio, gas gravity, API gravity and reservoir Temperature) are available; both compositional data and bulk properties are available. Through literature review, there is no research on using only compositional data and temperature to predict bubble point pressure. Our super learner model offers an accurate solution for oil bubble point pressure when only compositional data and temperature are available. Machine learning models perform better than empirical correlations with limited input data (i.e., bulk properties). When compositional data and bulk properties are all used as predictors, super learner reaches about 5.146% mean absolute relative error on predicting the bubble point pressure from global samples with bubble point pressures in the range of 100 to 10,000 psi, which is a wider range compared to most ANN models published in literature.