Research On Measuring Signal Processing Method Of Dynamic Point-the-Bit Rotary Steerable Drilling Tool

Taking the prototype of the dynamic point-the-bit rotary steerable system(DPRSS)independently developed as the research object,based on the analysis of the noise characteristics of the gyroscope and the three-axis gravity accelerometer,the processing method of the measuring signal is deeply studied to realize accurate measurement of the stable platform speed and the gravity tool-face angle(GTA).Firstly,the research status of rotary steerable drilling tool at home and abroad is introduced.The working principles of different rotary steerable drilling tools are compared and the advantages of DPRSS are analyzed.The research objectives of the project are clarified.Secondly,the directional drilling parameters and the working principle of DPRSS are introduced,and the measurement system is specially introduced.The noise characteristics of the three-axis gravity accelerometer and gyroscope under vibration condition are analyzed.Thirdly,for the accelerometer motion acceleration noise,a dual three-axis gravity accelerometer processing method is proposed.For the colored noise component in the accelerometer additional noise,an exponential weighted average algorithm and an IIR filtering algorithm are proposed.Both filtering algorithms can achieve the noise reduction effect,but the IIR filtering algorithm can flexibly select the amplitude-frequency characteristics.Consequently,the IIR filtering algorithm is preferred to process the measurement signal.For the gyroscope constant drift,a zero point voltage correction method is proposed.For the gyroscope random drift,the Kalman filtering algorithm based on ARMA model and IIR filtering algorithm are proposed.The data processing results show that the random drift ARMA model is difficult to establish when the stable platform rotates,and the practicality of the Kalman filtering algorithm is limited.The IIR filtering algorithm has better performance for tracking the dynamic characteristics of the signal.Consequently,the IIR filtering algorithm is better for processing measurement signals.Fourthly,for the white noise in the three-axis gravity accelerometer measurement signal,a complementary filtering algorithm and an adaptive Kalman filtering algorithm are proposed.The processing results show that both filtering algorithms can reduce the influence of white noise.Because the latter can adaptively adjust internal parameters according to working conditions,it is more practical.Consequently,the adaptive Kalman filtering algorithm is preferred to fuse the data to accommodate working condition changes during the drilling process.Finally,based on the above processing results,a measurement signal processing system of DPRSS is proposed,and a more accurate GTA is obtained.The adaptive Kalman filtering algorithm is improved.The simulated and measured data filtering results show that the adaptive Kalman filtering algorithm can estimate the state variables more accurately under the condition that the process noise variance matrix estimation is not negative.