References:
1. Simon C., Vázquez G. Use of Big Data and Machine Learning to Optimise Operational Performance and Drill Bit Design. SPE Asia Pacific Oil & Gas Conference and Exhibition. 2020. DOI: 10.2118/202243-MS
2. Dmitrievsky A.N., Eremin N.A., Safarova E.A., Filippova D.S., Borozdin S.O. Qualitative Analysis of Time Series GeoData to Prevent Complications and Emergencies During Drilling of Oil and Gas Wells. SOCAR Proceedings. 2020. № 3. pp. 31–37. (In Russ.). DOI: 10.5510/OGP20200300442
3. Bimastianto P.A., Khambete S.P., Alsaadi H.M., Al Ameri S.M., Couzigou E., Al-Marzouqi A.A.R., Al Ameri F.S., Aboulaban S., Khater H., Herve Ph. Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, 2021. DOI: 10.2118/207987-MS
4. Dmitrievsky A.N., Sboev A.G., Eremin N.A., Chernikov A.D., Naumov A.V., Gryaznov A.V., Moloshnikov I.A., Borozdin S.O., Safarova E.A. On increasing the productive time of drilling oil and gas wells using machine learning methods. Georesursy = Georesources. 2020. Vol. 22. № 4, pp. 79–85. (In Russ.). DOI: 10.18599/grs.2020.4.79-85
5. Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design. Journal of Petroleum Technology. December 2021. Available at: https://jpt.spe.org/big-data-and-machine-learning-optimize-operational-performance-and-drill-bit-design (accessed: May 29, 2022).
6. Chernikov A.D., Eremin N.A., Stolyarov V.E., Sboev A.G., Semenova-Chashchina O.K., Fitsner L.K. Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions. Georesursy = Georesources. 2020. Vol. 22. № 3. pp. 87–96. (In Russ.). DOI: 10.18599/grs.2020.3.87-96
7. Rakhimov R.R., Zhdaneev O.V., Frolov K.N., Babich M.P. Stuck Pipe Early Detection on Extended Reach Wells Using Ensemble Method of Machine Learning. SPE Russian Petroleum Technology Conference. 2021. DOI: 10.2118/206516-MS
8. Arkhipov A.I., Dmitrievskiy A.N., Eremin N.A., Chernikov A.D., Borozdin S.O., Safarova E.A., Seynaroev M.R. Data quality analysis of the station of geological and technological researches in recognizing losses and kicks to improve the prediction accuracy of neural network algorithms. Neftyanoe Khozyaystvo = Oil Industry. 2020. № 8. pp. 63–67. (In Russ.). DOI: 10.24887/0028-2448-2020-8-63-67
9. Othman E.B., Gomes D., Tengku B., Tengku E.B., Meor H., Meor M.H., Yusoff M.H., Arriffin M.F., Ghazali R. Application of Machine Learning to Augment Wellbore Geometry-Related Stuck Pipe Risk Identification in Real Time. Offshore Technology Conference Asia. Kuala Lumpur, 2022. DOI: 10.4043/31695-MS
10. Borozdin S., Dmitrievsky A., Eremin N., Arkhipov A., Sboev A., Chashchina-Semenova O., Fitzner L., Safarova E. Drilling Problems Forecast System Based on Neural Network. SPE Annual Caspian Technical Conference. 2020. DOI: 10.2118/202546-MS
11. Zhu Qi. Treatment and Prevention of Stuck Pipe Based on Artificial Neural Networks Analysis. Offshore Technology Conference Asia. Kuala Lumpur, 2022. DOI: 10.4043/31693-MS
12. Bahlany S., Maharbi M., Zakwani S., Busaidi F., Benvenuti F. STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, 2021. DOI: 10.2118/207823-MS
13. Romberg E., Fisher A., Mazza J., Niedz C., Wehner B., Zhou A. Predicting Trouble Stages with Geomechanical Measurements and Machine Learning: A Case Study on Southern Midland Basin Horizontal Completions. SPE Annual Technical Conference and Exhibition. 2020. DOI: 10.2118/201699-MS
14. Iversen F.P., Thorogood J.L., Macpherson J.D., Macmillan R.A. Business Models and KPIs as Drivers for Drilling Automation. SPE Intelligent Energy International Conference and Exhibition. Aberdeen, 2016. DOI: 10.2118/181047-MS
2. Dmitrievsky A.N., Eremin N.A., Safarova E.A., Filippova D.S., Borozdin S.O. Qualitative Analysis of Time Series GeoData to Prevent Complications and Emergencies During Drilling of Oil and Gas Wells. SOCAR Proceedings. 2020. № 3. pp. 31–37. (In Russ.). DOI: 10.5510/OGP20200300442
3. Bimastianto P.A., Khambete S.P., Alsaadi H.M., Al Ameri S.M., Couzigou E., Al-Marzouqi A.A.R., Al Ameri F.S., Aboulaban S., Khater H., Herve Ph. Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, 2021. DOI: 10.2118/207987-MS
4. Dmitrievsky A.N., Sboev A.G., Eremin N.A., Chernikov A.D., Naumov A.V., Gryaznov A.V., Moloshnikov I.A., Borozdin S.O., Safarova E.A. On increasing the productive time of drilling oil and gas wells using machine learning methods. Georesursy = Georesources. 2020. Vol. 22. № 4, pp. 79–85. (In Russ.). DOI: 10.18599/grs.2020.4.79-85
5. Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design. Journal of Petroleum Technology. December 2021. Available at: https://jpt.spe.org/big-data-and-machine-learning-optimize-operational-performance-and-drill-bit-design (accessed: May 29, 2022).
6. Chernikov A.D., Eremin N.A., Stolyarov V.E., Sboev A.G., Semenova-Chashchina O.K., Fitsner L.K. Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions. Georesursy = Georesources. 2020. Vol. 22. № 3. pp. 87–96. (In Russ.). DOI: 10.18599/grs.2020.3.87-96
7. Rakhimov R.R., Zhdaneev O.V., Frolov K.N., Babich M.P. Stuck Pipe Early Detection on Extended Reach Wells Using Ensemble Method of Machine Learning. SPE Russian Petroleum Technology Conference. 2021. DOI: 10.2118/206516-MS
8. Arkhipov A.I., Dmitrievskiy A.N., Eremin N.A., Chernikov A.D., Borozdin S.O., Safarova E.A., Seynaroev M.R. Data quality analysis of the station of geological and technological researches in recognizing losses and kicks to improve the prediction accuracy of neural network algorithms. Neftyanoe Khozyaystvo = Oil Industry. 2020. № 8. pp. 63–67. (In Russ.). DOI: 10.24887/0028-2448-2020-8-63-67
9. Othman E.B., Gomes D., Tengku B., Tengku E.B., Meor H., Meor M.H., Yusoff M.H., Arriffin M.F., Ghazali R. Application of Machine Learning to Augment Wellbore Geometry-Related Stuck Pipe Risk Identification in Real Time. Offshore Technology Conference Asia. Kuala Lumpur, 2022. DOI: 10.4043/31695-MS
10. Borozdin S., Dmitrievsky A., Eremin N., Arkhipov A., Sboev A., Chashchina-Semenova O., Fitzner L., Safarova E. Drilling Problems Forecast System Based on Neural Network. SPE Annual Caspian Technical Conference. 2020. DOI: 10.2118/202546-MS
11. Zhu Qi. Treatment and Prevention of Stuck Pipe Based on Artificial Neural Networks Analysis. Offshore Technology Conference Asia. Kuala Lumpur, 2022. DOI: 10.4043/31693-MS
12. Bahlany S., Maharbi M., Zakwani S., Busaidi F., Benvenuti F. STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi, 2021. DOI: 10.2118/207823-MS
13. Romberg E., Fisher A., Mazza J., Niedz C., Wehner B., Zhou A. Predicting Trouble Stages with Geomechanical Measurements and Machine Learning: A Case Study on Southern Midland Basin Horizontal Completions. SPE Annual Technical Conference and Exhibition. 2020. DOI: 10.2118/201699-MS
14. Iversen F.P., Thorogood J.L., Macpherson J.D., Macmillan R.A. Business Models and KPIs as Drivers for Drilling Automation. SPE Intelligent Energy International Conference and Exhibition. Aberdeen, 2016. DOI: 10.2118/181047-MS