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

By Xi Yang, B. Dindoruk, Ligang Lu. 2020

68 citations recorded by the source corpus.

Abstract The need for fluid properties or PVT (Pressure-Volume-Temperature) properties, is part of the entire Exploration and Production (EP 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.

Subfields: Analytical Chemistry, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/67a912c2d5e5e9e8129d057d6b9af4dc370ab4e2