A Robust Mag/INS-Based Orientation Estimation Algorithm for Measurement While Drilling
In order to obtain an optimal orientation of the bottom hole assembly while drilling, a novel robust Mag/INS-based orientation estimation algorithm, designed to support a magnetic/ inertial-based measurement while drilling (MWD) surveying system, is proposed and assessed. First, a magnetic/inertial-...
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| Veröffentlicht in: | IEEE sensors journal Jg. 17; H. 23; S. 7971 - 7980 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1530-437X, 1558-1748 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | In order to obtain an optimal orientation of the bottom hole assembly while drilling, a novel robust Mag/INS-based orientation estimation algorithm, designed to support a magnetic/ inertial-based measurement while drilling (MWD) surveying system, is proposed and assessed. First, a magnetic/inertial-based MWD surveying system has been designed by employing triaxial accelerometers, triaxial magnetometers, and triaxial gyroscopes. Second, in the Kalman filter framework the orientation-dependent components, i.e., the Earth's gravity field and the Earth's magnetic field, are decoupled from the vibration and magnetic disturbances respectively based on Coriolis transfer equation. Third, in the federal Kalman filter framework, the extracted gravity and magnetic field measurements are robustly fused together with angular velocity readings from the gyroscopes by employing the failure detection and inter(FDI) technique. Semi-physical simulation and actual drilling experiments show that the proposed novel robust Mag/INS-based orientation algorithm adapts well to the severe conditions of the drilling environment and can effectively improve measurement precision and reduce errors produced by drillstring vibration and magnetic disturbances. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1530-437X 1558-1748 |
| DOI: | 10.1109/JSEN.2017.2761853 |