Yang Yi, Li Fei, Gao Yi, Mao Yanhui
School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China.
Sensors (Basel). 2020 Mar 29;20(7):1897. doi: 10.3390/s20071897.
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.
在导向钻井系统姿态测量过程中，由于服务器振动对钻头的影响，姿态参数的测量可能具有不确定性和不可预测性。为了消除振动对测量的干扰并快速获取导向钻井工具准确的姿态参数，提出了一种多传感器动态姿态联合测量新方法。首先，利用三轴加速度计和三轴磁强计测量系统，建立基于四元数的非线性模型。然后，提出一种改进的自适应渐消平方根无迹卡尔曼滤波器来消除振动干扰信号。在该算法中，用状态协方差矩阵的平方根代替经典无迹卡尔曼滤波器（UKF）中的协方差矩阵，以避免因状态协方差矩阵为负定而导致滤波器发散。将渐消因子引入UKF以实时调整滤波器增益，提高算法对突变状态的自适应能力。最后，对渐消因子的计算方法进行优化，以确保算法的自适应性并降低计算复杂度。实验室测试结果和现场钻井数据表明，所提方法能有效滤除姿态测量传感器中的干扰噪声，在测量环境突变情况下提高钻井工具姿态参数的解算精度，从而确保井眼轨迹的动态稳定性。
