Abstract
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%.
Highlights
IntroductionHorizontal drilling processes make use of Measurement While Drilling (MWD) instruments to monitor the position and the orientation of the bottom hole assembly (BHA)
In the oil industry, horizontal drilling processes make use of Measurement While Drilling (MWD) instruments to monitor the position and the orientation of the bottom hole assembly (BHA).The traditional MWD instrument is comprised of three accelerometers and three magnetometers.The magnetometers determine the orientations by measuring the earth magic field
This paper proposed a new noise modeling approach for vibration noise and applied this new
Summary
Horizontal drilling processes make use of Measurement While Drilling (MWD) instruments to monitor the position and the orientation of the bottom hole assembly (BHA). In Reference [11], the stochastic errors in the inertial sensors were assumed to be a white noise process. In reference [13], the random noise of the Micro-Electro-Mechanical System (MEMS) inertial sensor were identified and modeled by Allan variance. This model was applied into this low cost Inertial navigation system (INS) integrated system to improve the accuracy and performance of the system. The gyroscope vibration noise is identified by DAVAR, and a noise model based on DAVAR was developed for the first time With this accurate vibration noise model, the performance and the accuracy of the KF could be improved.
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