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This study presents machine learning models that forecast and categorize lostcirculation severity preemptively using a large class imbalanced drillingdataset. We demonstrate reproducible core techniques involved in tackling alarge drilling engineering challenge utilizing easily interpretable machinelearning approaches.  We utilized a 65,000+ records data with class imbalance problem from Azadeganoilfield formations in Iran. Eleven of the dataset's seventeen parameters arechosen to be used in the classification of five lost circulation events. Togenerate classification models, we used six basic machine learning algorithmsand four ensemble learning methods. Linear Discriminant Analysis (LDA),Logistic Regression (LR), Support Vector Machines (SVM), Classification andRegression Trees (CART), k-Nearest Neighbors (KNN), and Gaussian Naive Bayes(GNB) are the six fundamental techniques. We also used bagging and boostingensemble learning techniques in the investigation of solutions for improvedpredicting performance. The performance of these algorithms is measured usingfour metrics: accuracy, precision, recall, and F1-score. The F1-score weightedto represent the data imbalance is chosen as the preferred evaluationcriterion.  The CART model was found to be the best in class for identifying drillingfluid circulation loss events with an average weighted F1-score of 0.9904 andstandard deviation of 0.0015. Upon application of ensemble learning techniques,a Random Forest ensemble of decision trees showed the best predictiveperformance. It identified and classified lost circulation events with aperfect weighted F1-score of 1.0. Using Permutation Feature Importance (PFI),the measured depth was found to be the most influential factor in accuratelyrecognizing lost circulation events while drilling.

A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset