A review of stuck-pipe prediction: from physical mechanisms to the evolution of integrated intelligence
Abstract
Stuck-pipe is one of the most destructive downhole incidents in oil drilling operations, causing billions of dollars in losses to the global oil and gas industry each year. Current stuck-pipe prediction methods face a dilemma regarding the trade-off between real-time early warning, accuracy, and interpretability. This dilemma has persisted throughout the history of prediction methods, driving a paradigm shift from empirical formulas to black-box models and ultimately to physics-aware intelligence. This paper proposes a three-dimensional adaptation framework that connects the mechanisms of stuck-pipe, data frequency, and adaptive prediction methods. For example, pack-off is identified as a high-frequency, progressive type of stuck pipe, and data-driven methods are used for prediction. Based on this foundation, the paper provides a systematic review of the technical characteristics and evolutionary logic of three categories of methods: physical modeling, data-driven approaches, and hybrid fusion. Then, it provides an in-depth analysis of three major data challenges: frequency misalignment, label scarcity, and distribution skew. It discusses the paradigm shift in evaluation from statistical accuracy to engineering value. Finally, the paper looks ahead to cutting-edge directions such as digital twins, transfer learning, incremental learning, and multimodal fusion. This paper aims to provide a systematic reference for theoretical innovation and technological application in the field of stuck-pipe prediction.
Document Type: Original article
Cited as: Li, Z., Hu, P., Huang, X. A review of stuck pipe prediction: Evolution from physical mechanism analysis to integrated intelligence. Sustainable Earth Resources Communications, 2026, 2(1): 37-49. https://doi.org/10.46690/serc.2026.01.03
DOI:
https://doi.org/10.46690/serc.2026.01.03Keywords:
Drilling, drilling safety, machine learning, stuck-pipe, stuck-pipe predictionReferences
Aadnoy, B. S., Looyeh, R. Petroleum rock mechanics: drilling operations and well design. Gulf professional publishing, 2019.
Abbas, A. K., Flori, R., Almubarak, H., Dawood, J., Abbas, H., Alsaedi, A. Intelligent Prediction of Stuck Pipe Remediation Using Machine Learning Algorithms. Presented at the SPE Annual Technical Conference and Exhibition, SPE, Calgary, Alberta, Canada, 2019.
Abdali, M. R., Mohamadian, N., Ghorbani, H., Wood, D. A. Petroleum well blowouts as a threat to drilling operation and wellbore sustainability: causes, prevention, safety and emergency response. Journal of Construction Materials, 2021, 2652 3752.
Ahmed, O. S., Aman, B. M., Zahrani, M. A., Ajikobi, F. I. Stuck Pipe Early Warning System Utilizing Moving Window Machine Learning Approach, Presented at Abu Dhabi International Petroleum Exhibition and Conference. SPE, 2019.
AitAli, R., Arevalo, P., Dashevskiy, D., Mahmoud, M., Pocaterra, A., Al Bouny, A., Hussain, M. Q., Baruno, A., Mohammed, S. Hybrid Data Driven Intelligent Algorithm for Stuck Pipe Prevention, Presented at ADIPEC. Presented at the ADIPEC, SPE, Abu Dhabi, UAE, 2023.
Akpedeye, K. U. Advancement on Drilling Technology in Petroleum Industry, 2011.
Al-Baiyat, I., Heinze, L. Implementing Artificial Neural Networks and Support Vector Machines in Stuck Pipe Prediction, in: SPE Kuwait International Petroleum Conference and Exhibition. Presented at the SPE Kuwait International Petroleum Conference and Exhibition, SPE, Kuwait City, Kuwait, 2012.
Al-Mamoori, H. N., Tian, J., Ma, H. Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis. Applied Sciences, 2025, 15: 5042.
Alshaikh, A., Magana-Mora, A., Gharbi, S. A., Al-Yami, A. Machine Learning for Detecting Stuck Pipe Incidents: Data Analytics and Models Evaluation, in: International Petroleum Technology Conference. Presented at the International Petroleum Technology Conference, IPTC, Beijing, China, 2019.
Alshaikh, A. A., Albassam, M. K., Al Gharbi, S. H., Al-Yami, A. S. Detection of Stuck Pipe Early Signs and the Way Toward Automation, in: Abu Dhabi International Petroleum Exhibition & Conference. Presented at the Abu Dhabi International Petroleum Exhibition & Conference, SPE, Abu Dhabi, UAE, 2018
Alshaikh, A. A., Amanullah, M. A comprehensive review of differential sticking, spotting fluids, and the current testing and evaluation methods. Presented at SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. SPE, 2018.
Altindal, M. C., Nivlet, P., Tabib, M., Rasheed, A., Kristiansen, T. G., Khosravanian, R. Anomaly detection in multivariate time series of drilling data. Geoenergy Science and Engineering, 2024, 237: 212778.
Alzahrani, M., Alotaibi, B., Aman, B. Novel Stuck Pipe Troubles Prediction Model Using Reinforcement Learning, in: International Petroleum Technology Conference. Presented at the International Petroleum Technology Conference, IPTC, Riyadh, Saudi Arabia, 2022.
Bailey, L., Jomnes, T., Belaskie, J., Orban, J., Sheppard, M., Houwen, O., Jardine, S., McCann, D. Stuck pipe: causes, detection and prevention. Oilfield Review, 1991, 3(4): 13-26.
Bhatia, G., Yadav, A., Nanda, D., Goyal, D., Perumalla, S., Shinde, A., Jha, B., Upreti, D. Digitization of Daily Drilling Reports Using LLMs. Presented at the Middle East Oil, Gas and Geosciences Show (MEOS GEO), OnePetro. 2025.
Bo, C., Yu, S., Feng, X., Fukang, P. Development of Time Series Drilling Datasets for Stuck Pipe Prediction Using Volve Field Data, in: Volume 8: Offshore Geotechnics; Petroleum Technology. Presented at the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering, American Society of Mechanical Engineers, Singapore, Singapore, 2024.
Cao, B., Song, Y., Yang, J., Li, L., n.d. Early signs of stuck pipe detection based on Crossformer. arXiv 2025, 2503.07440. Available online: https://arxiv.org/abs/2503.07440 (accessed on March 2026)
Chamkalani, A., Pordel Shahri, M., Poordad, S., n.d. Support Vector Machine Model: A New Methodology for Stuck Pipe Prediction. Presented at SPE Unconventional Gas Conference and Exhibition, Muscat, Oman, January 2013.
Chen, T., Guestrin, C. XGBoost: A Scalable Tree Boosting System. Presented at Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. Association for Computing Machinery, New York, NY, USA, 2016.
Cho, H., Shah, S. N., Osisanya, S. O. A three-segment hydraulic model for cuttings transport in horizontal and deviated wells. Presented at SPE/CIM International Conference on Horizontal Well Technology. SPE, 2000.
Deng, S., Pan, H. -Y., Wang, H. -G., Xu, S. -K., Yan, X. -P., Li, C. -W., Peng, M. -G., Peng, H. -P., Shi, L., Cui, M., Zhao, F. A hybrid machine learning optimization algorithm for multivariable pore pressure prediction. Petroleum Science, 2024, 21: 535–550.
Duan, M., Miska, S., Yu, M., Takach, N., Ahmed, R., Zettner, C. 2006. Transport of small cuttings in extended reach drilling. Paper SPE-104192 Presented at SPE International Oil and Gas Conference and Exhibition, Beijing, China, 5–7 December 2006.
Dupriest, F. E., Jr., William, C. E., Ottesen, S. Design Methodology and Operational Practices Eliminate Differential Sticking. Presented at the IADC/SPE Drilling Conference and Exhibition, New Orleans, Louisiana, USA, February 2010.
Elkatatny, S. Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks. Arabian Journal for Science and Engineering, 2017, 42, 1655–1665.
Epelle, E. I., Gerogiorgis, D. I. A review of technological advances and open challenges for oil and gas drilling systems engineering. AIChE Journal, 2020, 66, e16842.
Forest-Bize, N., Jarrot, A., Legendre, E., Quesada, D., Le Nir, I., Shrivastava, C., Brill, T. M. Bridging the Gap Between Geologists and Drillers: LWD Ultrasonic Imaging Goes that Extra Mile. Presented at the SPWLA 63rd Annual Logging Symposium, OnePetro, 2022.
Fu, J., Liu, W., Deng, S., Zou, Y., Hao, W., Huang, P., Li, P., Liang, L. An Intelligent Prediction Model for Stuck Pipe Driven by a Hybrid of Physics and Data. Presented at the International Field Exploration and Development Conference 2024, Xi’an, China, 12–14 September 2024.
Gomes, D., Jaritz, T., Robinson, T. S., Revheim, O. E. Enhancing Stuck Pipe Risk Detection in Exploration Wells Using Machine Learning Based Tools: A Gulf of Mexico Case Study. Presented at IADC/SPE International Drilling Conference and Exhibition. Presented at the IADC/SPE International Drilling Conference and Exhibition, SPE, Galveston, Texas, USA, 5–7 March 2024.
Graves, A. Long Short-Term Memory, in Supervised Sequence Labelling with Recurrent Neural Networks, Studies in Computational Intelligence. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 37–45.
Gulsrud, T. O., Nybø, R., Bjørkevoll, K. S. Statistical Method for Detection of Poor Hole Cleaning and Stuck Pipe. Presented at SPE Offshore Europe Oil and Gas Conference and Exhibition, Aberdeen, UK, 8–11 September 2009.
Hegde, C., Wallace, S., Gray, K. Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling. Presented at the SPE Middle East Intelligent Oil and Gas Conference and Exhibition, SPE, Abu Dhabi, UAE, 15–16 September 2015.
Hempkins, W. B., Kingsborough, R. H., Lohec, W. E., Nini, C. J. Multivariate Statistical Analysis of Stuck Drillpipe Situations. SPE Drilling Engineering, 1987, 2, 237–244.
Hu, Y., Yang, Y. A Comparative Study on Drag Reduction Methods for Continental Shale Drilling in the Fuxing Block, Southeastern Sichuan Basin. Reservoir Science, 2026, 2, 81–96.
Hummes, O., Bond, P., Jones, A., Symons, W., Bishop, M., Serdy, A., Pokrovsky, S., Polito, N. Using advanced drilling technology to enable well factory concept in the Marcellus shale. Paper SPE-151466 Presented at SPE/IADC Drilling Conference and Exhibition. SPE, San Diego, California, USA, 6–8 March 2012.
Jahanbakhshi, R., Keshavarzi, R., Aliyari Shoorehdeli, M., Emamzadeh, A. Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields. SPE Drill & Completion, 2012, 27, 586–595.
Johancsik, C. A., Friesen, D. B., Dawson, R. Torque and drag in directional wells-prediction and measurement. Journal of Petroleum technology, 1984, 36, 987–992.
Kamp, A. M., Rivero, M. Layer modeling for cuttings transport in highly inclined wellbores. Paper SPE-53942 Presented at SPE Latin America and Caribbean Petroleum Engineering Conference, Caracas, Venezuela, 21–23 April 1999.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T. -Y. LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Advances in Neural Information Processing Systems. Curran Associates, Inc., New York, USA, 2017.
Kharlamov, S. N., Al-obaidi, S. S. Analysis of pipe sticking at well drilling in the South Iraqi limestone and sandstone reservoirs. Izvestiya, 2024, 335, 91–111.
Kizayev, T., Irawan, S., Khan, J. A., Khan, S. A., Cai, B., Zeb, N., Wayo, D. D. K. Factors affecting drilling incidents: Prediction of suck pipe by XGBoost model. Energy Reports, 2023, 9, 270-279.
Lavin, A., Ahmad, S. Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark. Presented at the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 09-11 December 2015.
LeCun, Y., Bengio, Y., Hinton, G. Deep learning. Nature, 2015, 521, 436–444.
Liu, F. T., Ting, K. M., Zhou, Z. -H. Isolation forest. Presented at 2008 Eighth Ieee International Conference on Data Mining, Pisa, Italy, 15-19 December 2008.
Liu, M., Song, X., Zhu, Z., Li, G., Xiao, H., Fu, L., Pan, T., Li, X., Yang, Y. Inertial Effect of Drill Pipe Friction Trend and Early Warning of Stuck Pipe. Paper D021S015R004 Presented at the SPE Annual Technical Conference and Exhibition, SPE, New Orleans, Louisiana, USA, 23–25 September 2024.
Liu, M. -C., Zhu, Z. -P., Song, X. -Z., Yang, Y. -L., Pan, T., Yan, Z., Zhou, D. -T. Physics-informed autoencoder with Bayesian optimization for real-time stuck pipe risk prediction. Petroleum Science, 2026, in press.
Lundberg, S. M., Lee, S. -I. A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017, 30.
Mahmood, A. T. K., Assi, A. H. A Comprehensive Review of the Pipe Sticking Mechanism in Oil Well Drilling Operations. Jcoeng, 2024, 30, 50–70.
Miao, H., Qiu, Z., Dokhani, V., Ma, Y., Zhang, D. Numerical modeling of transient cuttings transport in deviated wellbores. Geoenergy Science and Engineering, 2023, 227, 211875.
Millheim, K. K., Apostal, M. C. The effect of bottomhole assembly dynamics on the trajectory of a bit. Journal of Petroleum Technology, 1981, 33, 2323–2338.
Miri, R., Sampaio, J., Afshar, M., Lourenco, A. Development of Artificial Neural Networks To Predict Differential Pipe Sticking in Iranian Offshore Oil Fields. Presented at International Oil Conference and Exhibition in Mexico, Veracruz, Mexico, 27–30 June 2007.
Mitchell, B. Advanced oilwell drilling engineering handbook. 1992.
Mitchell, R. F., Allen, M. B. Lateral vibration: the key to BHA failure analysis. World Oil 1985, 200.
Montes, A. C., Ashok, P., van Oort, E. Review of Stuck Pipe Prediction Methods and Future Directions. Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 23–25 September 2024.
Mopuri, K. R., Bilen, H., Tsuchihashi, N., Wada, R., Inoue, T., Kusanagi, K., Nishiyama, T., Tamamura, H. Early sign detection for the stuck pipe scenarios using unsupervised deep learning. Journal of Petroleum Science and Engineering, 2022, 208, 109489.
Nakagawa, Y., Inoue, T., Bilen, H., Mopuri, K. R., Miyoshi, K., Abe, S., Wada, R., Kuroda, K., Nishi, M., Ogasawara, H. Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture. Paper D041S112R001 Presented at the Abu Dhabi International Petroleum Exhibition & Conference, SPE, Abu Dhabi, UAE, 15–18 November 2021.
Nezhad, M. M., Ashoori, S., Hooshmand, P., Mirzaee, M. Stuck Drill Pipe Prediction with Networks Neural in Maroon Field. 2012.
Nugroho, W. A., Hermawan, S., Lazuardi, B. H., Mirza, R. Drilling problems mitigation in geothermal environment, case studies of stuck pipe and lost circulation. Paper D031S004R004 Presented at SPE Asia Pacific Oil and Gas Conference and Exhibition, SPE, Jakarta, Indonesia, 17–19 October 2017.
Outmans, H. D. Mechanics of differential pressure sticking of drill collars. Transactions of the AIME, 1958, 213, 265–274.
Pal, K., Mangla, V. K., Joshi, N. P., Tewari, P. P., Bose, U. N. A new approach solves differential sticking problems in Geleki and Ahmedabad fields. Paper SPE-63055 Presented at SPE Annual Technical Conference and Exhibition SPE, Dallas, Texas, USA, 1–4 October 2000.
Pan, S. J., Yang, Q. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 2009, 22, 1345–1359.
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., Wermter, S. Continual lifelong learning with neural networks: A review. Neural Networks, 2019, 113, 54–71.
Pearl, J. Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier 2014.
Qu, J., Sun, X., Yan, T., Chen, Y., Hu, Q. A novel prediction model for cuttings bed height in horizontal and deviated wells. Journal of Natural Gas Science and Engineering, 2021, 95, 104192.
Raissi, M., Perdikaris, P., Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 2019, 378, 686–707.
Rosiani, D., Suranta, B. Y., Sofyan, A., Pradana, F. G., Putra, R. B. Machine Learning Classifies Data for Early Warning of Stuck Pipe Detection in Geothermal Drilling. International Journal on Advanced Science, Engineering & Information Technology, 2025, 15, 44-51.
Rostami, H., Khaksar Manshad, A. A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields. Journal of Energy Resources Technology, 2014, 136, 024502.
Sampaio, J. H., Lourenco, A. A hybrid Model for predicting the likelihood of differential sticking in offshore wells. Paper OTC-24462 Presented at Offshore Technology Conference Brasil, Rio de Janeiro, Brazil, 29–31 October 2013.
Shokouhi, S. V., Skalle, P., Aamodt, A., Sørmo, F. Integration of Real-time Data and Past Experiences for Reducing Operational Problems. Presented at the IPTC 2009: International Petroleum Technology Conference, European Association of Geoscientists & Engineers, December 2009.
Skalle, P., Aamodt, A., Gundersen, O. E. Detection of Symptoms for Revealing Causes Leading to Drilling Failures. SPE Drill & Completion, 2013, 28, 182–193.
Sorgun, M. A mathematical model for estimating cuttings bed thickness in horizontal and deviated wells. Ijogct, 2017, 16, 329.
Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., Gottschlich, J. Precision and Recall for Time Series, in: Advances in Neural Information Processing Systems. Curran Associates, Inc., New York, USA, 2018
Tsuchihashi, N., Wada, R., Ozaki, M., Inoue, T., Mopuri, K. R., Bilen, H., Nishiyama, T., Fujita, K., Kusanagi, K. Early Stuck Pipe Sign Detection with Depth-Domain 3D Convolutional Neural Network Using Actual Drilling Data. SPE Journal 2021, 26, 551–562.
Uche, O., Christian, O., Hasib, M., Ogwal, E. Role of Drilling Fluids in Solving Drilling Challenges: A Major Focus on Plugged Drill Strings. International Journal of oil, gas and Coal Engineering, 2025, 13, 27–36.
Wang, J., Guan, Z., Liu, M., Song, X., Wang, Z. Drilling Stuck Probability Intelligent Prediction Based on LSTM Considering Local Interpretability. Presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023.
William, J. K. M., Gupta, P., Sangwai, J. S. Interaction of lubricants on the rheological and filtration loss properties of water-based drilling fluids. Petroleum Science and Technology, 2021, 39, 235–248.
Wu, H., Wang, Z., Si, L., Zou, X., Gu, J., Wei, D., Tan, C. A sticking predictor construction and evaluation method for drill tools sticking prediction. Review of Scientific Instruments, 2025, 96, 015116.
Wu, J., Ansari, U. From CO$_2$ Sequestration to Hydrogen Storage: Further Utilization of Depleted Gas Reservoirs. Reservoir Science, 2025, 1, 19–35.
Xia, B., Wang, Y., Li, Q., Song, X., Zhu, Z., Liu, M., Yang, Y. Research on Stuck Pipe Prediction Based on Supervised and Unsupervised Ensemble Learning. Processes, 2025, 13, 3309.
Yang, Y., Huang, F., Kang, S. Mechanism of Penetration Rate Improvement in Hot Dry Rock Under the Coupling of Impact Load and Confining Pressure Release. Reservoir Science, 2026, 2, 52–64.
Zadeh, L. A. Fuzzy sets. Information and control, 1965, 8, 338–353.
Zakerian, A., Sarafraz, S., Tabzar, A., Hemmati, N., Shadizadeh, S. R. Numerical modeling and simulation of drilling cutting transport in horizontal wells. Journal of Petroleum Exploration and Production Technology, 2018, 8, 455–474.
Zhang, J., Wang, H., Ji, G., Zhuo, L., Zhao, Z., Liu, X., Chen, W. Digital Twin System Utilizing Hybrid Approach of Physics Model and Machine Learning for Pipe Stuck Early Identification and Warning Based on Real-Time Data. Presented at the Offshore Technology Conference Asia, OnePetro, Kuala Lumpur, Malaysia, February 2024.
Zhang, T., Xue, Y., Meng, Z., Sader, M., Zhang, W., Li, J. Drill Sticking Prediction Based on Modal Decomposition and Physical Constraint Model of Near-Bit Data. Processes 2005, 13, 1802.
Zhang, X., Dong, P., Yang, Y., Zhang, Q., Sun, Y., Song, X., Zhu, Z. Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM. Processes, 2024, 12, 1296.
Zhaoxuan, L., Ting, Y., Ming, W., Jun, C., Jin, L., Huan, C. The Latest Advancements in Technology for Intelligent Drilling and Completion Equipment Research. Journal of Liaoning University of Petroleum & Chemical Technology, 2024, 44, 73.
Zhu, Q., Wang, Z., Huang, J. Stuck Pipe Incidents Prediction Based On Data Analysis. Presented at SPE Gas & Oil Technology Showcase and Conference, Dubai, UAE, October 2019.
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