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.
| Published in | Petroleum Science and Engineering (Volume 9, Issue 2) |
| DOI | 10.11648/j.pse.20250902.17 |
| Page(s) | 120-128 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Drilling Anomalies, Stuck Pipe Detection, Kick Detection, Machine Learning, Random Forest, Real-Time Monitoring, Hook Load
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APA Style
Iorkyaa, A. J., Igbo, E. F., James, N. J. (2025). Supervised Learning Models for Detection of Kick and Stuck Pipe During Drilling Operations in Complex Formations. Petroleum Science and Engineering, 9(2), 120-128. https://doi.org/10.11648/j.pse.20250902.17
ACS Style
Iorkyaa, A. J.; Igbo, E. F.; James, N. J. Supervised Learning Models for Detection of Kick and Stuck Pipe During Drilling Operations in Complex Formations. Pet. Sci. Eng. 2025, 9(2), 120-128. doi: 10.11648/j.pse.20250902.17
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Download@article{10.11648/j.pse.20250902.17,
author = {Aondofa Jacob Iorkyaa and Emmanuel Fred Igbo and Nkpoikana Joseph James},
title = {Supervised Learning Models for Detection of Kick and Stuck Pipe During Drilling Operations in Complex Formations
},
journal = {Petroleum Science and Engineering},
volume = {9},
number = {2},
pages = {120-128},
doi = {10.11648/j.pse.20250902.17},
url = {https://doi.org/10.11648/j.pse.20250902.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250902.17},
abstract = {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.
},
year = {2025}
}
TY - JOUR T1 - Supervised Learning Models for Detection of Kick and Stuck Pipe During Drilling Operations in Complex Formations AU - Aondofa Jacob Iorkyaa AU - Emmanuel Fred Igbo AU - Nkpoikana Joseph James Y1 - 2025/10/28 PY - 2025 N1 - https://doi.org/10.11648/j.pse.20250902.17 DO - 10.11648/j.pse.20250902.17 T2 - Petroleum Science and Engineering JF - Petroleum Science and Engineering JO - Petroleum Science and Engineering SP - 120 EP - 128 PB - Science Publishing Group SN - 2640-4516 UR - https://doi.org/10.11648/j.pse.20250902.17 AB - 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. VL - 9 IS - 2 ER -
Department of Petroleum Engineering, University of Uyo, Uyo, Nigeria
Department of Petroleum Engineering, University of Uyo, Uyo, Nigeria
Department of Electrical and Electronics Engineering, University of Uyo, Uyo, Nigeria
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