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Vibration Noise Modeling for Measurement While Drilling System Based on FOGs

机译：基于FOG的随钻测量振动噪声建模

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摘要

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%.
机译：为了长期提高基于光纤陀螺仪（FOG）的随钻测量（MWD）的测量精度，通过卡尔曼滤波器（KF）方法将外部辅助源融合到惯性导航中。 KF方法需要将惯性传感器的噪声建模为系统噪声模型。传统上，系统噪声被建模为高斯白噪声。但是，由于钻进时的振动，陀螺仪中的噪声不再是高斯白噪声。此外，错误的噪声模型会降低KF的精度。本文开发了一种基于动态艾伦方差（DAVAR）的噪声建模新方法。与传统的白噪声模型相比，新的噪声模型同时包含白噪声和色噪声。利用这种新的噪声模型，设计了随钻测井的KF。最后，进行了两个振动实验。实验结果表明，所提出的振动噪声建模方法显着提高了惯性传感器漂移的估计精度。将基于不同噪声模型的导航结果进行比较，使用DAVAR噪声模型可以将位置误差和工具面角度误差降低90％以上。速度误差降低了65％以上。方位角误差减少了50％以上。

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