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Vibration Noise Modeling for Measurement While Drilling System Based on FOGs.

Chunxi Zhang1, Lu Wang2, Shuang Gao3

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Summary

This study enhances Measurement While Drilling (MWD) accuracy by improving how sensor noise is modeled. Using dynamic Allan variance (DAVAR) for fiber optic gyroscope noise, the new method significantly reduces navigation errors.

Keywords:
FOG-based MWDdynamic Allan variancesystem noise modelvibration noise

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Area of Science:

  • Geophysics
  • Sensor Technology
  • Navigation Systems

Background:

  • Measurement While Drilling (MWD) systems rely on inertial navigation, often using Fiber Optic Gyroscopes (FOGs).
  • Kalman filtering (KF) is standard for fusing external aiding sources but requires accurate sensor noise modeling.
  • Conventional white Gaussian noise models are inadequate for the non-white noise generated by drilling vibrations, degrading KF accuracy.

Purpose of the Study:

  • To develop an improved noise modeling approach for FOGs in MWD applications, specifically addressing drilling-induced vibrations.
  • To enhance the accuracy of inertial navigation systems used in MWD by incorporating a more realistic sensor noise model.

Main Methods:

  • Developed a novel noise modeling approach based on dynamic Allan variance (DAVAR).
  • Implemented a Kalman filter (KF) for MWD navigation using the new DAVAR-based noise model, which accounts for both white and color noise.
  • Conducted two vibration experiments to validate the proposed method.

Main Results:

  • The DAVAR-based noise model significantly improved the accuracy of inertial sensor drift estimation.
  • Compared to conventional models, the DAVAR noise model reduced position error and toolface angle error by over 90%.
  • Velocity error was reduced by more than 65%, and azimuth error by over 50%.

Conclusions:

  • The dynamic Allan variance (DAVAR) approach provides a more accurate model for gyroscope noise under drilling vibrations than traditional white noise models.
  • Implementing this advanced noise model within a Kalman filter framework substantially enhances the overall accuracy of MWD navigation.
  • This research offers a significant improvement for MWD survey accuracy, particularly in challenging downhole environments.