Estimating pore fluid saturation in an oil sands reservoir using ensemble tree machine learning algorithms

Abstract

This thesis aims to estimate pore fluid saturation values in an oil sands reservoir using ensemble tree based machine learning models. Oil sands reservoirs provide an interesting opportunity to explore a relatively new technique in petrophysical analysis. The specific reservoir used in this study has high heterogeneity with discrete muddy layers that are difficult and time consuming to incorporate into a conventional petrophysical model. In addition, due to strong well control and sufficient well log data, the reservoir is a perfect candidate to test out a data-driven model by using techniques in Machine Learning – a subfield of Artificial Intelligence. Specifically, Random Forests and Extreme Gradient Boosted Trees are combined, which are two different ways to implement a decision-tree based model structure. The two algorithms have rapidly gained popularity in the machine learning community due to their robustness when dealing with outliers and/or bad data combined with a comparative immunity against over-fitting. The final aim of this thesis is to obtain comparable or superior results to the Modified Simandoux Equation method and analyze the shortcomings and advantages of the two methods in a real petroleum field.