Loading

Digitalization (Drilling Dynamics)

09 Mar 2022
Grand Ballroom B
Technical Session
Session Chairs
Junichi Sugiura - Sanvean Technologies
Deep Joshi - Corva

Site Sort Order

17

Proposal

  • 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

Proposal JSON

[{"proposaltitle":"Automated Merging of Time Series and Textual Operations Data To Extract Technical Limiter Re-Design Recommendations","TechnicalDiscipline1":"1. Drilling","proposalnumber":208745,"SessionRoleStatus":"Primary","proposaltime":"1600-1630","authorblock":"P. Ashok, M. Yi, D. Ramos, Intellicess Inc.; S. Bohlander, T.S. Thetford, M. Behounek, Apache Corp."},{"proposaltitle":"Maximizing the Value of Downhole Drilling Data: A Novel Approach to Digital Drilling Data Management and Analytics","TechnicalDiscipline1":"1. Drilling","proposalnumber":208710,"SessionRoleStatus":"Primary","proposaltime":"1630-1700","authorblock":"M.R. Isbell, J. Neal, H. Copeland, Hess Corp.; N. Foster, S. Patrick, Fracture ID"},{"proposaltitle":"Progress Toward an Open-Source Drilling Community: Contributing and Curating Models","TechnicalDiscipline1":"1. Drilling","proposalnumber":208794,"SessionRoleStatus":"Primary","proposaltime":"1700-1730","authorblock":"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"},{"proposaltitle":"A Hybrid Physics-Based and Machine-Learning Approach for Stick/Slip Prediction","TechnicalDiscipline1":"8. Data Science and Engineering Analytics","proposalnumber":208760,"SessionRoleStatus":"Alternate","proposaltime":" ","authorblock":"P. Sheth, I. Roychoudhury, C. Chatar, J.R. Celaya Galvan, Schlumberger"},{"proposaltitle":"Prediction of Stuck Pipe Incidents Using Models Powered by Deep Learning and Machine Learning","TechnicalDiscipline1":"8. Data Science and Engineering Analytics","proposalnumber":208778,"SessionRoleStatus":"Alternate","proposaltime":" ","authorblock":"A. Mal, S. Ødegård, S. Helgeland, S.Z. Sinaga, M. Svendsen, eDrilling"},{"proposaltitle":"How Deep Learning Can Provide Consistent Improvement on ROP Through Different Drilling Environments","TechnicalDiscipline1":"1. Drilling","proposalnumber":208743,"SessionRoleStatus":"Alternate","proposaltime":" ","authorblock":"D.A. Junca Rivera, J. Bohorquez Gutierrez, E. Dontsova, J. Estrada-Giraldo, J. Martinez Ferreira, J. Webb, Enovate Upstream"},{"proposaltitle":"Behavior Anomalies Detection In Drilling Time Series Through Feature Extraction","TechnicalDiscipline1":"1. Drilling","proposalnumber":208676,"SessionRoleStatus":"Alternate","proposaltime":" ","authorblock":"C. Jeong, Y. Yu, D.F. Patino, S. Venkatakrishnan, D. Mansour, Schlumberger"},{"proposaltitle":"Real-time Reamer Vibration Predicting, Monitoring, and Decision-Making Using Hybrid Modeling and a Process Digital Twin","TechnicalDiscipline1":"1. Drilling","proposalnumber":208795,"SessionRoleStatus":"Alternate","proposaltime":" ","authorblock":"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"}]

Host Organisation

Equinor

Diversity and Inclusion Session Co-Sponsor

 

 

NOV