Formation lithology classification using scalable gradient boosted decision trees

The classification of underground formation lithology is an important task in petroleum exploration and engineering since it forms the basis of geological research studies and reservoir parameter calculations. Hence, there have recently been increased efforts to automate lithology classification by incorporating various data science tools and principles. In this regard, efforts were made recently to evaluate machine learning methods to classify formation lithology by using data from the Daniudui gas field (DGF) and Hangjinqi gas field (HGF), both located in China. Although the boosted ensemble learners utilized in the studies performed well, there is still scope for improvement with respect to the prediction metrics. Additionally, the issue of scalability of some of these algorithms is also of concern. Hence, building upon the success of these algorithms in the previous studies, we tap into the state of the art of scalable ensemble decision tree algorithms, in our study. Specifically, we applied recently developed gradient boosted decision tree (GBDT) systems, namely, XGBoost, LightGBM and CatBoost, after combining well log data obtained from DGF and HGF. We compare their performance with random forests (RFs), AdaBoost and gradient boosting machines (GBMs) which serve as a baseline. We evaluated the algorithms using metrics such as the micro average, macro average and weighted average of precision (Pr), recall (Re) and F1-score (F1) on the test set after hyperparameter tuning. In our analysis, among the applied algorithms, we found that LightGBM possessed the highest metrics. Our work identifies LightGBM and CatBoost as good first-choice algorithms for the supervised classification of lithology when utilizing well log data. (C) 2019 Elsevier Ltd. All rights reserved.