随机加权渐消UKF在导向钻井姿态测量中的应用

Application of random weighted fading UKF on attitude measurement for rotary steerable system

针对近钻头钻具易受井下强振动等随机噪声的影响,使得导向钻具姿态动态测量误差较大的问题,提出一种随机加权渐消UKF算法并应用于导向钻井姿态测量系统。通过Sigma点获取状态估值和协方差矩阵,采用随机加权因子动态调整不同时刻残差向量的权值;再用渐消因子实时调节滤波增益,限制滤波器的记忆长度,有效利用最新量测信息对状态更新的影响调整预测误差协方差阵。将提出的算法应用到导向钻具姿态测量系统进行验证,结果表明,所提算法解算后得到的井斜角误差控制在±0.25°以内,工具面角误差控制在±1°以内,与标准EKF和UKF相比,井斜角解算误差和工具面角解算误差均降低了50%以上,能有效提高导向钻具动态测量精度。

In practice,the near-bit signals are usually accompanied by noises generated by drilling tool vibration and high-speed rotary,which makes the attitude dynamic measurement error of the drilling tool larger.A random weighted fading unscented Kalman filter algorithm is proposed and applied to the attitude measurement of rotary steerable system.The state estimation and covariance matrix are obtained by Sigma points,and the weights of residual vectors at different times are dynamically adjusted by random weighting factors.The fading factors are used to adjust the filter gain in real time and limit the memory length of the filter.They can effectively adjust the prediction error covariance matrix by taking advantage of the influence of the latest measurement information on the state update.The proposed algorithm is applied to the attitude measurement of rotary steerable system,the results show that the error of inclination angle obtained by the proposed algorithm is less than±0.25°,and the error of tool face angle is less than±1°.Compared with the standard EKF and UKF algorithms,the error of inclination angle and the error of tool face angle are reduced by more than 50%,which can effectively improve the dynamic measurement accuracy for rotary steerable system.