Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman...

Area of Science:

• Geophysics
• Mechanical Engineering
• Control Systems

Background:

• Steering drilling systems require precise attitude measurement.
• Vibration interference from drill bits causes uncertainty in attitude parameters.
• Existing methods struggle with dynamic and unpredictable environmental changes.

Purpose of the Study:

• To develop a robust multi-sensor dynamic attitude measurement method for steering drilling tools.
• To eliminate vibration-induced noise and improve attitude parameter accuracy.
• To ensure the dynamic stability of well trajectories.

Main Methods:

• Established a nonlinear quaternion-based model using triaxial accelerometer and magnetometer data.
• Proposed an improved adaptive fading square root unscented Kalman filter (UKF).
• Optimized the fading factor computation for self-adaptability and reduced complexity.

Main Results:

• Successfully filtered out interference noise from attitude measurement sensors.
• Significantly improved the accuracy of attitude parameters, even with abrupt environmental changes.
• Demonstrated effective noise reduction and enhanced adaptive capabilities in laboratory and field tests.

Conclusions:

• The proposed multi-sensor fusion method effectively addresses vibration interference in attitude measurement.
• The improved adaptive fading square root UKF enhances accuracy and robustness.
• This method ensures reliable dynamic stability for well trajectories in steering drilling operations.