Vibration Noise Modeling for Measurement While Drilling System Based on FOGs

Aiming to improve survey accuracy of Measurement While Drilling (MWD) based on Fiber Optic Gyroscopes (FOGs) in the long period, the external aiding sources are fused into the inertial navigation by the Kalman filter (KF) method. The KF method needs to model the inertial sensors’ noise as the system noise model. The system noise is modeled as white Gaussian noise conventionally. However, because of the vibration while drilling, the noise in gyros isn’t white Gaussian noise any more. Moreover, an incorrect noise model will degrade the accuracy of KF. This paper developed a new approach for noise modeling on the basis of dynamic Allan variance (DAVAR). In contrast to conventional white noise models, the new noise model contains both the white noise and the color noise. With this new noise model, the KF for the MWD was designed. Finally, two vibration experiments have been performed. Experimental results showed that the proposed vibration noise modeling approach significantly improved the estimated accuracies of the inertial sensor drifts. Compared the navigation results based on different noise model, with the DAVAR noise model, the position error and the toolface angle error are reduced more than 90%. The velocity error is reduced more than 65%. The azimuth error is reduced more than 50%.

[1]  Pin Lv,et al.  A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration , 2014 .

[2]  Haiying Hou,et al.  Modeling inertial sensors errors using Allan variance , 2004 .

[3]  龙兴武 Long Xingwu,et al.  Research on Stochastic Errors of Dithered Ring Laser Gyroscope Based on Dynamic Allan Variance , 2010 .

[4]  Hyeon-Seok Lee,et al.  Influence of Dynamics and Trajectory on Integrated GPS/INS Navigation Performance , 2003 .

[5]  Song Shen-min.  Dynamic allan variance analysis for the drift error of fiber optical gyroscope , 2008 .

[6]  Anjani Kumar,et al.  Colored-noise Kalman filter for vibration mitigation of position/attitude estimation systems , 2007 .

[7]  Tao Wang,et al.  The Fast algorithm of the dynamic Allan variance for gyroscopes , 2016 .

[8]  Jinling Wang,et al.  Quantization and Colored Noises Error Modeling for Inertial Sensors for GPS/INS Integration , 2011, IEEE Sensors Journal.

[9]  Jang Gyu Lee,et al.  Performance Improvement of GPS/INS Integrated System Using Allan Variance Analysis , 2004 .

[10]  Yudan Yi,et al.  On improving the accuracy and reliability of GPS/INS-based direct sensor georeferencing , 2007 .

[11]  Martin P. Mintchev,et al.  Measurement-while-drilling surveying of highly inclined and horizontal well sections utilizing single-axis gyro sensing system , 2004 .

[12]  Tao Wang,et al.  Characterization of a fiber optic gyroscope in a measurement while drilling system with the dynamic Allan variance , 2015 .

[13]  Chunxi Zhang,et al.  A Long-Term Performance Enhancement Method for FOG-Based Measurement While Drilling , 2016, Sensors.

[14]  Dorota A. Grejner-Brzezinska,et al.  On Improving Navigation Accuracy of GPS/INS Systems , 2005 .

[15]  P. Tavella,et al.  The dynamic Allan variance , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[16]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[17]  Chen Ming-hui.  Study of Zero Velocity Update for Inertial Navigation , 2008 .

[18]  Naser El-Sheimy,et al.  Modeling Inertial Sensor Errors Using Autoregressive (AR) Models , 2004 .

[19]  Zhang Na.  Analysis of dynamic characteristics of a fiber-optic gyroscope based on dynamic Allan variance , 2011 .

[20]  Lorenzo Galleani,et al.  Application of the Dynamic Allan Variance for the Characterization of Space Clock Behavior , 2011, IEEE Transactions on Aerospace and Electronic Systems.