Supervised Learning Models for Detection of Kick and Stuck Pipe During Drilling Operations in Complex Formations

In the oil and gas industry, the drilling phase is most critical in planning and controlling as it faces several problems which require accurate and prompt responses to limit all sort of losses. Among these challenges, kicks and stuck pipe incidents represent two of the most costly and disruptive problems, with their early detection and control very essential to improve efficiency and ensure safety. In this paper, four supervised learning techniques namely: Logistic Regression (LR), Gradient Boosting (GB), Decision Tree (DT), and Random Forest (RF), were applied to a time-series dataset comprising 275,000 data points (sampled at 10-second intervals) from the Forge 16B (78)-32 well. Anomalies, including kick, stuck pipe, and normal drilling conditions were labeled within the dataset by setting appropriate conditions of exceeding thresholds/limits using python code. Unlike most studies, we employed twenty-one (21) input parameters to improve effectiveness of each parameter for robust model development. From the results, RF achieved the highest performance, with an accuracy, precision, and recall of 0.998. The DT model followed closely, scoring 0.996 across the same metrics; GB model recorded 0.962 for accuracy, 0.963 for precision, and 0.962 for recall, and LR had the lowest values of all the four metrics. Feature importance analysis identified hook load (klbs), rate of penetration (ft/hr) and weight on bit (klbs) as the most influential parameters for anomaly detection, in descending order of relevance. By leveraging on a focused dataset and high-level detection semantics, this work offers a sturdy, interpretative, and highly reliable framework for real-time anomaly detection, paving the way for more efficient and safer drilling operations in challenging formations.