Sequential binary classification of lithofacies from well-log data and their uncertainty quantification

Abstract Understanding and identifying the composition of various lithofacies in the subsurface is essential for successful reservoir characterization in hydrocarbon exploration. However, conventional methods such as core sampling and manual well-log interpretation are labor-intensive. As a result, many scientists are conducting research to use machine learning to study lithofacies more effectively and efficiently. However, as researchers are becoming more dependent on machine learning, an uncertainty analysis of machine-learning models is crucial to determine the reliability of the prediction results. Machine-learning algorithms that use ensemble methods provide an easy method for an uncertainty analysis but algorithms that do not use ensemble methods have difficulty in quantifying the level of uncertainty. In our research, we introduce a method known as sequential binary classification (SBC), which helps not only classify lithofacies but also to quantify and visualize the regions of uncertainty of the machine-learning models. SBC provides a method for using any classification algorithm of the user’s choice to construct an ensemble, which allows the user to quantify uncertainty readily. Our research uses the SBC algorithm to classify and quantify the uncertainty from the well-log data obtained from the North Sea near Norway. The results show that most of the lithofacies that exist in the region of interest share similar characteristics, which results in high uncertainty among the various lithofacies, and SBC helps to visualize these high uncertainties. We additionally demonstrate how SBC alleviates the class imbalance issue among the various lithofacies in the area, which is a very common problem in well-log data analytics.