AI-Driven Non-Productive Time Reduction in Saudi Drilling Operations: Predictive Detection of Wellbore Instability, Stuck Pipe and Downhole Equipment Failures

References

[1] Xu K, et al. Application of machine learning in wellbore stability prediction: A review. Geoenergy Science and Engineering. 2024;232:212409. https://doi.org/10.1016/j.geoen.2023.212409

[2] Kamgue Lenwoue AR, et al. Recent advances and challenges of the application of artificial intelligence to predict wellbore instabilities during drilling operations. SPE Drilling & Completion; 2023. https://doi.org/10.2118/215830-PA

[3] Mopuri KR, et al. Early sign detection for the stuck pipe scenarios using unsupervised deep learning. Journal of Petroleum Science and Engineering. 2022;208:109489. https://doi.org/10.1016/j.petrol.2021.109489

[4] Elahifar B, Hosseini E. Machine learning algorithm for prediction of stuck pipe incidents using statistical data: Case study in Middle East oil fields. Journal of Petroleum Exploration and Production Technology. 2022;12:2019–2045. https://doi.org/10.1007/s13202-021-01436-3

[5] Brankovic A, et al. Data-driven indicators for the detection and prediction of stuck-pipe events in oil and gas drilling operations. Upstream Oil and Gas Technology. 2021;7:100043. https://doi.org/10.1016/j.upstre.2021.100043

[6] D’Amicis S, et al. Stuck pipe prediction from rare events in oil drilling operations. Upstream Oil and Gas Technology. 2023;11:100096. https://doi.org/10.1016/j.upstre.2023.100096

[7] Siqueira VSM, et al. Expert system for early sign stuck pipe detection: Feature engineering and fuzzy logic approach. Engineering Applications of Artificial Intelligence. 2024;127:107229. https://doi.org/10.1016/j.engappai.2023.107229

[8] Zhu S, et al. Intelligent prediction of stuck pipe using combined data-driven and knowledge-driven model. Applied Sciences. 2022;12:5282. https://doi.org/10.3390/app12105282

[9] Aseel A, et al. Analysis of pipe sticking due to wellbore uncleanliness using machine learning. Heliyon. 2023;9:e22366. https://doi.org/10.1016/j.heliyon.2023.e22366

[10] Al Dushaishi MF, et al. Data-driven stuck pipe prediction and remedies. Upstream Oil and Gas Technology. 2021;6:100024. https://doi.org/10.1016/j.upstre.2020.100024

[11] Pang H, et al. Lost circulation prediction based on machine learning. Journal of Petroleum Science and Engineering. 2022;208:109364. https://doi.org/10.1016/j.petrol.2021.109364

[12] Wood DA, Mardanirad S, Zakeri H. Effective prediction of lost circulation from multiple drilling variables: A class imbalance problem for machine and deep learning algorithms. Journal of Petroleum Exploration and Production Technology. 2022;12:83–98. https://doi.org/10.1007/s13202-021-01411-y

[13] Alsaihati A, et al. Application of machine learning methods in modeling the loss of circulation rate while drilling operation. ACS Omega. 2022;7:20696–20709. https://doi.org/10.1021/acsomega.2c00970

[14] Wu J, et al. Artificial neural network prediction of wellbore stability in offshore shallow formations. Geoenergy Science and Engineering. 2024;213322. https://doi.org/10.1016/j.geoen.2024.213322

[15] Elkatatny S, Al-AbdulJabbar A, Abdelgawad K. A new model for predicting rate of penetration using an artificial neural network. Sensors. 2020;20:2058. https://doi.org/10.3390/s20072058

[16] Alsaihati A, Elkatatny S, Gamal H. Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model. Journal of Petroleum Science and Engineering. 2022;208:109335. https://doi.org/10.1016/j.petrol.2021.109335

[17] Zhong R, Salehi C, Johnson R. Machine learning for drilling applications: A review. Journal of Natural Gas Science and Engineering. 2022;108:104807. https://doi.org/10.1016/j.jngse.2022.104807

[18] Elmgerbi A, Thonhauser G. Holistic autonomous model for early detection of downhole drilling problems in real-time. Process Safety and Environmental Protection. 2022;164:418–434. https://doi.org/10.1016/j.psep.2022.06.035

[19] Gurina E, et al. Making the Black-box brighter: Interpreting machine learning algorithm for forecasting drilling accidents. Journal of Petroleum Science and Engineering. 2022;218:111041. https://doi.org/10.1016/j.petrol.2022.111041

[20] Tunkiel AT, Sui D, Wiktorski T. Impact of data pre-processing techniques on recurrent neural network performance in context of real-time drilling logs in an automated prediction framework. Journal of Petroleum Science and Engineering. 2022;208:109760. https://doi.org/10.1016/j.petrol.2021.109760

[21] Wang C, et al. Downhole working conditions analysis and drilling complications detection method based on deep learning. Journal of Natural Gas Science and Engineering. 2020;81:103485. https://doi.org/10.1016/j.jngse.2020.103485

[22] Ye S, Song X, Blunt MJ, Bijeljic B. Domain adaptation neural network based prediction of real-time drill bit tooth wear. Geoenergy Science and Engineering. 2024;213041. https://doi.org/10.1016/j.geoen.2024.213041

[23] Agostini CE, Sampaio MA. Probabilistic neural network with Bayesian-based, spectral torque imaging and deep convolutional autoencoder for PDC bit wear monitoring. Journal of Petroleum Science and Engineering. 2020;193:107434. https://doi.org/10.1016/j.petrol.2020.107434

[24] Wang X, et al. Automated classification of drill string vibrations using machine learning algorithms. Geoenergy Science and Engineering. 2024;239:212995. https://doi.org/10.1016/j.geoen.2024.212995

[25] Boukredera FS, et al. Enhancing the drilling efficiency through the application of machine learning and optimization algorithm. Engineering Applications of Artificial Intelligence. 2023;126:107035. https://doi.org/10.1016/j.engappai.2023.107035

[26] Saadeldin R, Gamal H, Elkatatny S. Machine learning solution for predicting vibrations while drilling the curve section. ACS Omega. 2023;8:35822–35836. https://doi.org/10.1021/acsomega.3c03413

[27] Aljohar FE, Contreras Otalvora WB. Enhancing drilling equipment reliability: Deep learning for predicting failure time and real-time anomaly detection. ADIPEC Technical Paper, SPE-221928-MS; 2024. https://doi.org/10.2118/221928-MS

[28] Zomarah B, et al. Early warning method to detect mud motor stallings incidents in drilling operations using machine learning. International Petroleum Technology Conference, IPTC-24183-MS; 2024. https://doi.org/10.2523/IPTC-24183-MS

[29] Al-Mamoori HN, 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. https://doi.org/10.3390/app15095042

[30] Sui D, Sahebi H. Trend and dynamic analysis on temporal drilling data and their data-driven models. Geoenergy Science and Engineering. 2023;211530. https://doi.org/10.1016/j.geoen.2023.211530

@article{1724177,
      author = {Qamar ur zaman},
      title = {AI-Driven Non-Productive Time Reduction in Saudi Drilling Operations: Predictive Detection of Wellbore Instability, Stuck Pipe and Downhole Equipment Failures},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3078-3089},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724177.pdf},
      abstract = {Drilling non-productive time arises when mechanical dysfunction, unstable formations and failing equipment interrupt planned progress. Predictive analytics could shorten the interval between an emerging precursor and a suitable intervention, yet model discrimination is not equivalent to verified operational savings. This critical review examines published research from 2020 to 2025 on stuck-pipe precursors, geomechanical instability, drilling-fluid loss, harmful vibrations and downhole component degradation, with Saudi Arabian operating conditions as the principal application context. Evidence is compared through signal availability, physical plausibility, prediction horizon, dataset separation and intervention feasibility. The analysis identifies complementary rather than interchangeable functions for geomechanical models, temporal anomaly detectors, vibration classifiers and component-health estimators. It proposes a risk-gated monitoring architecture in which sensor quality checks, uncertainty estimates and mechanistic screening precede actionable recommendations. A distinction is maintained between laboratory findings, historical-well validations and prospective reductions in rig downtime. The resulting framework prioritises interpretable early warnings, crew authority, reliable telemetry and post-deployment evaluation. Its principal contribution is an integrated decision pathway connecting the forecast event, a proportionate operational response and measurable avoided disruption rather than a single universal prediction score.},
      keywords = {non-productive time; drilling analytics; stuck pipe; geomechanics; tool reliability; Saudi Arabia; early warning},
      month = {October},
      doi = {https://doi.org/10.64388/IREV10I4-1724177}
  }