17

• 1600-1630208745Automated Merging of Time Series and Textual Operations Data To Extract Technical Limiter Re-Design Recommendations
  P. Ashok, M. Yi, D. Ramos, Intellicess Inc.; S. Bohlander, T.S. Thetford, M. Behounek, Apache Corp.
• 1630-1700208710Maximizing the Value of Downhole Drilling Data: A Novel Approach to Digital Drilling Data Management and Analytics
  M.R. Isbell, J. Neal, H. Copeland, Hess Corp.; N. Foster, S. Patrick, Fracture ID
• 1700-1730208794Progress Toward an Open-Source Drilling Community: Contributing and Curating Models
  R.J. Shor, S.S. Kandala, University of Calgary; E. Gildin, S.F. Noynaert, E.Z. Losoya, V. Kesireddy, N. Vishnumolakala, Texas A&M University; I. Kim, MathWorks; J. Ng, Pason Systems Corporation; J.K. Wilson, Scientific Drilling International; E. Cayeux, NORCE; R. Dixit, ExxonMobil Services & Technology Pvt Ltd; G.S. Payette, ExxonMobil Upstream Research; T. Cunningham, P.E. Pastusek, ExxonMobil Upstream Integrated Solutions Co
• 208760A Hybrid Physics-Based and Machine-Learning Approach for Stick/Slip Prediction
  P. Sheth, I. Roychoudhury, C. Chatar, J.R. Celaya Galvan, Schlumberger
• 208778Prediction of Stuck Pipe Incidents Using Models Powered by Deep Learning and Machine Learning
  A. Mal, S. Ødegård, S. Helgeland, S.Z. Sinaga, M. Svendsen, eDrilling
• 208743How Deep Learning Can Provide Consistent Improvement on ROP Through Different Drilling Environments
  D.A. Junca Rivera, J. Bohorquez Gutierrez, E. Dontsova, J. Estrada-Giraldo, J. Martinez Ferreira, J. Webb, Enovate Upstream
• 208676Behavior Anomalies Detection In Drilling Time Series Through Feature Extraction
  C. Jeong, Y. Yu, D.F. Patino, S. Venkatakrishnan, D. Mansour, Schlumberger
• 208795Real-time Reamer Vibration Predicting, Monitoring, and Decision-Making Using Hybrid Modeling and a Process Digital Twin
  J. Shi, L. Dourthe, D. Li, L. Deng, L. Louback, F. Song, N.I. Abolins, F. Verano, P. Zhang, J. Groover, D. Gomez Falla, K. Li, Schlumberger

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