| 杨金显,蔡纪鹏,尹凤帅,王赛飞,袁旭瑶.随钻测量钻具重力加速度提取方法[J].电子测量与仪器学报,2023,37(10):145-152 |
| 随钻测量钻具重力加速度提取方法 |
| Extraction method of gravity acceleration of drilling tool while measuring |
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| DOI： |
| 中文关键词: 随钻测量 FAUKF 重力提取 抗磁因子 抗振因子 井斜角 |
| 英文关键词:measurement while drilling federal adaptive unscented Kalman filter extraction of gravity information antimagnetic factor antivibration factor inclination |
| 基金项目:河南省自然科学基金(232300421152)、国家自然科学基金(41672363)项目资助 |
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| Author | Institution |
| Yang Jinxian | 1. School of Electrical Engineering and Automation, Henan Polytechnic University,2. Henan Key Laboratory of Intelligent Detection and Control of Coal Min Equipment |
| Cai Jipeng | 1. School of Electrical Engineering and Automation, Henan Polytechnic University,2. Henan Key Laboratory of Intelligent Detection and Control of Coal Min Equipment |
| Yin Fengshuai | 1. School of Electrical Engineering and Automation, Henan Polytechnic University,2. Henan Key Laboratory of Intelligent Detection and Control of Coal Min Equipment |
| Wang Saifei | 1. School of Electrical Engineering and Automation, Henan Polytechnic University,2. Henan Key Laboratory of Intelligent Detection and Control of Coal Min Equipment |
| Yuan Xuyao | 1. School of Electrical Engineering and Automation, Henan Polytechnic University,2. Henan Key Laboratory of Intelligent Detection and Control of Coal Min Equipment |
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| 中文摘要: |
| 针对随钻测量(MWD)中钻具重力加速度受振动干扰大引起的井斜角解算严重失真问题,提出基于联邦自适应无迹卡
尔曼滤波(FAUKF)的钻具重力加速度提取方法。 首先,建立无重置结构的联邦重力信息融合提取框架,选取基于陀螺仪数据
的递推重力值作为联邦滤波的公共参考值,分别与解耦的地磁数据观测的重力加速度组合作为子滤波器 1,与加速度计数据观
测的重力加速度组合作为子滤波器 2。 然后,对子滤波器的重力状态进行无迹卡尔曼滤波(UKF)算法,期间根据地磁数据观测
重力值的抗振性优于加速计的特性,设计子滤波器 2 对应的抗振因子,再根据陀螺仪短时精度高的输出特性,找到基于陀螺仪
数据解耦的地磁参考斜率值,来设计子滤波器 1 对应的抗磁因子,提升子滤波器性能,接着用自适应开窗因子来确定开窗估计
法则中的开窗值,调节新息协方差,通过对新息协方差的估计实现子滤波器量测噪声协方差的实时估计,提高无迹卡尔曼滤波
算法精度,进而得到可靠的重力信息局部自适应估计值。 最后通过联邦信息融合,进一步得到重力信息的全局估计。 通过模拟
钻进和实钻实验结果表明,FAUKF 算法和 FKF 算法相比,井斜角误差减小了± 1. 9°,FAUKF 算法下的井斜角误差可控制在
±1. 2°以内。 该方法可有效提取煤矿井下钻具重力加速度,提高井斜测量精度,是获得可靠钻具井斜角的有效方法。 |
| 英文摘要: |
| Aiming at the problem of serious distortion of inclination calculation caused by vibration interference of gravity acceleration of
drilling tool in MWD, a method of gravity acceleration extraction of drilling tool based on federal adaptive unscented Kalman filter
(FAUKF) is proposed. Firstly, a fusion extraction framework of federated gravity information without reset structure is established, and
the recursive gravity value based on gyroscope data is selected as the common reference value of federated filtering. The combined gravity
acceleration observed by decoupling geomagnetic data is used as sub-filter 1, and the combined gravity acceleration observed by
accelerometer data is used as sub-filter 2. Then, the untracked Kalman filter (UKF) algorithm is carried out on the gravity state of the
sub-filter. During this period, the vibration resistance of the gravity value observed according to the geomagnetic data is better than that
of the accelerometer. The antivibration factor corresponding to the sub-filter 2 is designed. The antimagnetic factor corresponding to the
sub-filter 1 is designed to improve the performance of the sub-filter, then the adaptive windowed factor is used to determine the windowed
value in the windowed estimation rule and adjust the new information covariance. The real-time estimation of the sub-filter measurement
noise covariance is achieved by estimating the new information covariance, and the accuracy of the untracked Kalman filter algorithm is
improved. Then reliable local adaptive estimates of gravity information are obtained. Finally, the global estimation of gravity information
is obtained by federal information fusion. The results of simulation drilling and real drilling experiments show that the inclination of
FAUKF algorithm is reduced by ±1. 9° compared with FKF algorithm, and the inclination of FAUKF algorithm can be controlled within ±1. 2°. This method can effectively extract the gravity acceleration of drilling tools in coal mine, improve the measurement accuracy of
inclination, and is an effective method to obtain reliable inclination. |
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