Primary funding for the Society of Petroleum Engineers Distinguished Lecturer Program is provided through member donations to the SPE Foundation and a contribution from Offshore Europe. Additional support comes from AIME. The program offers lectures from industry professionals on various topics, and is grateful to companies that allow their employees to participate as lecturers.
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John Hedengren
1. Primary funding is provided by
The SPE Foundation through member donations
and a contribution from Offshore Europe
The Society is grateful to those companies that allow their
professionals to serve as lecturers
Additional support provided by AIME
Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl
2. Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl
John D. Hedengren, Ph.D.
Brigham Young University
Drilling Automation and Downhole Monitoring
with Physics-based Models
3. Outline
• Why and how to automate drilling?
• 3 elements of automation
• Automation case study
• Closed loop downhole automation
• Challenges and opportunities: Physics-based or machine learned models
• Create fit-for-purpose digital twins for automation
• Unlock a new way of thinking
• Predictive versus reactive automation
• Combine pressure and rate control
• Conclusions
http://graphics8.nytimes.com
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4. Why Automate Drilling?
•Benefits of Automated Drilling
• HSE– faster response to problems
• Economic– operate closer to constraints,
shorter drilling time, especially with
challenging market conditions
• Average of 4 uncontrolled well situations in
the Gulf of Mexico each year (SPE 170756
& Morris, 2014)
http://graphics8.nytimes.com
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•Recent Success in Automation (SPE 184694, 139848, 150973, 73159)
• Greater accuracy and consistency
• Faster drilling with fewer interruptions
• More accurate directional drilling
9. How to Automate Drilling?
•Three elements for automation
• Sensor (measurement)
• Actuator (valve, motor, pump)
• Controller (computer)
•Considerations
• Open or proprietary
• Centralized or distributed
• Simple or complex
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Sensor Actuator Controller
Reservoir
Weight on
Bit
Rotation
Speed
Drill
String
Annulus
Back Pressure
Pump
Main Mud Pump
Choke
Valve
10. Transmit Downhole Sensor Data
10
Sensor
25 GB/hour
150,000 points/sec
51,200 GB/hr
Source: Simafore, Fortune, RTInsights, Cisco
Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic
Systems, Springer, 2017, DOI: 10.1007/s11081-015-9295-9.
12. Need Managed Pressure Drilling
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Sensor Depth
Pressure and Mud Density
Mud Density
Depth
Pressure and Mud Density
Mud Density
Wide Pressure Margins Narrow Pressure Margin
13. Managed Pressure Drilling Overview
Actuated Sensor Estimated
Mud Pump Flow Rate Bottom Hole Pressure Annulus Drill Fluid Density
Choke Valve Opening Annular Pressures Annulus Friction Factor
Back Pressure Pump Flow Choke Valve Pressure Reservoir Pore Pressure
Actuators for MPD and ROP
SPE-173045 13
Choke Valve Opening
Mud Pump Flow Rate
Pressure
Controller
Pressure Weight on Bit (WOB)
Rev per Minute (RPM)
ROP
Controller
Rate of Penetration (ROP)
Managed Pressure Drilling (MPD) Rate of Penetration (ROP)
Cuttings LoadingFluid Influx
14. Pressure Control Through Automation
• Normal drilling operations and a pipe connection
procedure, offshore SE Asia with WDP
• Choke pressure adjusted to control bit pressure
• Bit pressure
•±1 bar (±15 psi) during normal drilling
•±3 bar (±45 psi) during a pipe connection
14IADC/SPE 112651
15. Controllers for Automation
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•Sequence control for batch processes
•Feedback control for continuous systems
•Feedforward control anticipates disturbances
https://commons.wikimedia.org
FUTUREPAST
k
Actuator P+I+D+FF
P = proportional error
I = integral error
D = derivative error
FF = feedforward
17. FUTUREPAST
k
Conventional Feedback Control Model Predictive Control
Driving While Looking in Reverse
(Feedback)
Driving While Looking Forward
(Predictive or Feedforward)
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Improve with Predictive Automation
20. Challenge: Nonlinearity in Drilling
Increasing mud pump flow
Typical Operation Range
DownholePressure(bar)
Choke Valve Opening (%)
20
21. What are the Opportunities?
•Data-based Modeling
• Deep Learning / Artificial Intelligence
•Physics-based Modeling
• Wellbore Hydraulics
• Drill String Dynamics
•Optimize
• Computing hardware and optimization algorithms
• Optimization Benchmarks 2,500,000,000 times faster
• Moore’s law (17,000x faster) and optimizer
improvements (150,000x faster) in 30 years
Data
Optimize Models
Automation
21
29. ConventionalControl
Mud Pump Constant
Improved Pressure Control
ModelPredictiveControl
Lower Pump Limit Maintains Cuttings Transport 29
Flow
Excellent
setpoint
tracking
Choke
Pump Coordinates with Choke
Small Choke Adjustments
30. New Way of Thinking: Multivariate
Single-Input Single-Output Multi-Input Multi-Output
Conventional
Control Action
Set Point
Measurement
Model
Predictive
Control
Set point or Range
Conventional
Control Action
Set Point
Measurement
Measurements Control
Coordinated
Action
Multiple
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31. Automated Kick Response
Improvement with
Combined Control
Automation
Improvement
New Way of Thinking:
Pressure Control and Rate Optimization
Choke Valve Opening
Mud Pump Flow Rate
Pressure
Controller
Pressure Weight on Bit (WOB)
Rev per Minute (RPM)
ROP
Controller
Rate of Penetration (ROP)
Rev per Minute (RPM)
Weight on Bit (WOB)
Choke Valve Opening
Downhole Pressure
Rate of Penetration
Combined
Controller
Pump Flow Rates
SPE-170275
ROP 20% higher
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32. Challenges of Predictive Automation
•Requires digitization as a foundation
•Automation can become unreliable if the control model is not calibrated
or is not sufficiently accurate
•Advanced nonlinear, predictive controllers can fail to converge and result
in lost or poor control
•To address these challenges, reinforcement learning maintains model
accuracy and controller stability without interrupting the drilling process
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33. Conclusion
• Automation leads to:
•Greater accuracy and consistency
•Faster drilling with fewer interruptions
• Essential elements for automation: Sensors, Actuators, Controllers
• Predictive automation and optimization needs fit-for-purpose modeling
• Multivariate control coordinates among several actuators and objectives
• There are many challenges and opportunities for predictive automation
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34. Resources and Collaborations
•Open Source Drilling Models + Cases + Data
• Pastusek, P., Payette, G., Shor, R., Cayeux, E., Aarsnes, U.J., Hedengren, J.D.,
Menand, S., Macpherson, J., Gandikota, R., Behounek, M., Harmer, R.,
Detournay, E., Illerhaus, R., Liu, Y., Creating Open Source Models, Test Cases,
and Data for Oilfield Drilling Challenges, SPE/IADC Drilling Conference, The
Hague, Netherlands, March 2019, SPE-194082-MS.
https://github.com/APMonitor/drilling
•Online Course
•Machine Learning and Dynamic Optimization
•January-April 2019: https://apmonitor.com/do
•Connect on LinkedIn: https://www.linkedin.com/in/hedengren
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36. Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl 36
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