Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter

Abstract

In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.

Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

“…The main instruments and equipment for laboratory testing included a set of TX-3S inclinometer calibration devices, the six-degree space vibration experimental platform, and the data acquisition device, as shown in Figure 4 [ 2 ]. The other selected experimental equipment included a RIGOL DS1204B oscilloscope and a DC power supply.…”

“…The experimental data came from the field-drilling process of a well in northern Shaanxi. The field acquisition process and installation position of the triaxial accelerometer sensors are shown in Figure 8 [ 2 ], and the drilling and production environment parameters are listed in Table 4 . During drilling, the guiding tool was in a stable and straight state.…”

“…On the one hand, the frequency range of the noise presents the characteristics of wideness and randomness, which are shown in Table 1 ; on the other hand, the lateral vibration acceleration of the near-bit during drilling is generally about 10 g or up to 30 g [ 1 ], while the amplitude of the useful signal is generally less than 1 g. Therefore, the signal-to-noise ratio (SNR) of the attitude measurement signal is usually as low as −20 dB, or even lower. Thus, the useful signal is annihilated in the strong vibration background noise, which negatively interferes with the tool attitude measurement and makes the MWD invalid [ 2 , 3 ]. Multi-frequency, high amplitude noise interference and extremely low SNR are always the technical difficulties in the field of the dynamic measurement of steerable drilling tools.…”

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory

“…Consequently, the way to better handle multi-sensor data and improve data-fusion accuracy is a popular research direction in the field of data-fusion technology. Common data-fusion algorithms currently include Kalman filtering [ 2 ], Bayesian estimation [ 3 ], Dempster–Shafer (D-S) evidence theory [ 4 ], and artificial neural networks [ 5 ], etc. Bayesian networks and D-S evidence theory are commonly used to deal with the uncertainty in multi-sensor data, which frequently results in anomalous data.…”

Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

“…In 2009, Arasaratnam and Haykin established the Cubature Kalman Filter (CKF) based on the approximation of points [ 7 , 8 ]. Both the UKF and CKF use sigma interpolation to design a form similar to Kalman filter, so as to improve the influence caused by truncation error, and can achieve the second-order approximation [ 9 , 10 , 11 , 12 ]. However, no matter EKF, UKF, or CKF, it still cannot show better performance in strongly nonlinear systems.…”