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10 Drilling Mechanics III – Next Steps Towards Practical, Effective and Sustainable Solutions

Wednesday, 8 March
Mastrafjorden A
Technical Session
The industry continues to make giant positive strides in Drilling Optimisation, focusing on fundamental drilling concepts, physics-based discussions and a deeper understanding of all related disciplines – including Drilling Mechanics. This session, in addition to building onto what has already been achieved, challenges some of the industry’s currently held positions. Drilling dysfunctions are demystified based on innovative real-time and memory-based processes. Additionally, MSE shortfalls and limitations are identified and discussed, leading to new and appropriate recommendations for field deployment. Machine learning contributions to digital log generation, fluid circulation effects on Torque and Drag and process-based identification and avoidance of current drill-off-tests limitations will be discussed. In its totality, this must-see drilling mechanics session identifies drilling optimization challenges holistically. Most importantly, the discussions classify the next steps that must be tackled to achieve accelerated gains in drilling performance and project cost reductions.
Session Chairperson(s)
Graham Mensa-Wilmot - XCIDRILL Technology
Junichi Sugiura - Sanvean Technologies
  • 0900-0930 212504
    Drilling Dysfunction Demystified Using In Bit Strain Sensors
    Y. Witt-Doerring, Halliburton; P.E. Pastusek, ExxonMobil Upstream; A. Lacey, XTO Energy Inc.; P. Barajas, ExxonMobil Upstream; M. Bergeron, XTO Energy Inc.; D. Clayton, Halliburton; S.F. Sowers, XTO Energy Inc.
  • 0930-1000 212508
    Facts, Fallacies and Pitfalls of Using Mechanical Specific Energy (MSE) - Part 1
    R. Samuel, Halliburton; G. Mensa-Wilmot, XCIDrill Technology
  • 1000-1030 212558
    Fluid Circulation Effects on Torque and Drag Results, a New Take on an Old Subject
    M. Mahjoub, H.N. Dao, M. Summersgill, S. Menand, Helmerich & Payne
  • Alternate 212445
    Real-time Digital Log Generation from Drilling Parameters of Offset Wells Using Physics Informed Machine Learning
    P. Sheth, S. Sistla, I. Roychoudhury, M. Gao, C. Chatar, J.R. Celaya, P. Mishra, SLB
  • Alternate 212478
    Drill-off Tests for Drilling Optimization: Pitfalls and How to Avoid Them
    K. Chhantyal, Å. Kyllingstad, A. Hetland, K. Thor Birgisson, NOV

Host Organisation

Equinor

Platinum Partner
 

Saudi Aramco

Gold Partner
 

 

Transocean

Diversity & Inclusion Session Co-Sponsor

 

Chevron

Diversity & Inclusion Session Co-Sponsor

 

 

NOV

Diversity & Inclusion Session Co-Sponsor

 

Helmerich & Payne

Signage Sponsor

 

 

Baker Hughes

Lanyard Sponsor

 

RIVAL logo

Tech Talks Sponsor

 

Oliasoft

Day 1 Coffee Breaks Sponsor

 

KCA Deutag

Patron Sponsor

 

Neodrill