Leveraging the Self Driving Car Technology Stack to improve directional drilling engineering processes

1. Self Driving Directional Drilling on the Edge
SUMMARY
PARTICLE
FILTER
SEARCH
ALGORITHM
Using sensors, maps, geologic
markers / landmarks to
localize the car or BHA
Comparing Self Driving Car
and Directional Drilling
sensors and assemblies
Sensor Fusion Localization /
Geomagnetic Referencing
(Well) Path
Planning
Path Planning computed
navigating car / bit to target
Search Algorithm for
finding the optimal
well path
Accurate estimation of
geologic markers
provided by G&G,
estimated from LWD and
MWD
Control components of Cars (Steering, Brake, Throttle)
and Directional Drilling - Rotary Steerable System
Motion Control
Reach precise net pay
target zone
Edge Computing
METHODOLOGY
INTRODUCTION
● Self driving car technology involves applying robotic techniques such as
perception, sensor fusion, localization, path planning and motion control to
steer a car to the desired location safely
● Integrating with edge computing reduces network latency and allows
algorithms to run locally
● Filter Algorithms, Probabilistic Reasoning, Search Algorithms, Machine / Deep
Learning, Computer Vision, Big Data and IIoT all play a vital role
● How we can utilize the Self Driving Car Technology Stack and Edge Computing to improve
Directional Drilling Processes
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RESULTS
CONCLUSIONS
Safety is everyone’s topmost priority and deploying the self driving car
technology stack in controlled environments like drilling rig sites is much safer.
Other key economic benefits are:
● Accurate drilling measurement data
● Real time optimized well placement and trajectory planning
● Side tracks and dogleg severity reduction
● Geosteer multilateral well or ERD to target with higher precision
● Reduce expensive network communication and tripping costs between
surface and downhole environments
According to our research, leveraging edge computing and the self driving car
technology stack will improve directional drilling processes. They would utilize
these core components:
● Sensor Fusion:
Fusing telemetry data from LWD and MWD sensors to observe wellbore
environment using filter algorithms
● Localization:
Integrating maps, GPS and other data to determine the location of the drill bit
and BHA
● Well Placement and Trajectory Planning:
Generating realistic and stable well path for development and exploration wells in
real time based on our perception of the subsurface environment using
probabilistic and search algorithms
● Motion Control:
Achieve optimal rotation, sliding, steering of the drill bit to target net pay using
control engineering principles - PID, MPC. . .
● Edge Computing:
Deploying directional drilling intelligent agent on the edge of BHA electronics
With increased drilling activity, the need to improve and reduce costs
associated with directional drilling operations has arose. Currently, most of the
data used to execute these processes could be inaccurate. They are applied with
limited number of sensors that cannot determine the location of the drill bit
reliably and often times, the drilling process has to be halted to refresh the
model. This results in increased drilling/ rig time, wellbore tortuosity and the drill
bit’s inability to reach the target payzone precisely. Our research addresses such
challenges by leveraging the self driving car technology and edge computing.
This would reduce drilling/rig times,navigate the bit to its target reservoir and
mitigate systematic/human errors.
OBJECTIVE
Chijioke Ejimuda
Hybrid Data Solutions, Los Angeles, CA, USA
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SELECTED REFERENCES
Self Driving Directional Drilling
Technology Main Components
Thrun, S., Montemerlo, M. et al. 2006. Winning the DARPA Grand Challenge. Journal
of Field Robotics 2006. http://robots.stanford.edu/papers/thrun.stanley05.pdf
CONTROL
ALGORITHM
PID / MPC control
strategy to steer RSS
KALMAN
FILTER
Kalman filter
for sensor fusion [MWD, LWD. . .]
and state estimation
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