451525 High-Speed Data and High-Fidelity Models: Opportunities and Challenges in Well Manufacturing

Monday, April 11, 2016: 1:30 PM
339B (Hilton Americas - Houston)
Ammon Eaton, Junho Park, Sam Thorpe, Thomas Webber, Seyed Mostafa Safdarnejad and John D. Hedengren, Chemical Engineering, Brigham Young University, Provo, UT

The drilling industry is facing challenging market conditions that motivate the use of automation to reduce costs and decrease well manufacturing variability. Principles of smart manufacturing are being applied to achieve consistent and reliable performance in drilling that reduce variability and achieve target objectives. Two recent developments include reliable high speed data communication offered by wired drill pipe telemetry and advances in high fidelity modeling of borehole hydraulics and drillstring dynamics. This presentation explores opportunities and challenges in well manufacturing by combining high-speed data and high-fidelity models to both estimate and control critical drilling conditions. An ensemble control approach is proposed to automatically switch between high-fidelity and lower-order models in a similar manner to redundant mechanical or electrical backup systems that increase uptime and reliability.  When available, downhole measurements are also used to update the models with real-time moving horizon estimation to detect early warning signs of possible well manufacturing disruptions. An l1-norm objective function with a deadband for noise rejection is used to reject outliers and measurement drift. Unmeasured disturbances of interest include unexpected gas influx that can lead to blowouts or excessive cuttings loading that can lead to a pack-off of the rock cuttings and stuck drillpipe.  The ensemble model and measurement approach to smart well manufacturing has the potential to reduce drilling costs and improve consistency, especially in Managed Pressure Drilling (MPD) where tight pressure control and maximized rate of penetration are key performance indicators.

Extended Abstract: File Not Uploaded
See more of this Session: Smart Manufacturing: Advances in Modeling and Sensors
See more of this Group/Topical: Topical 3: Innovative Manufacturing