Subsurface Sensing Through Data Fusion of Redundant IMU Sensors with Supervised Learning

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dc.contributor.advisorPark, Simon S.
dc.contributor.advisorShor, Roman J.
dc.contributor.authorLiu, Huan
dc.contributor.committeememberKim, Jeong-woo
dc.contributor.committeememberRamírez Serrano, Alejandro
dc.contributor.committeememberGao, Yang
dc.contributor.committeememberChen, Dongmei
dc.date2019-11
dc.date.accessioned2019-09-23T16:43:39Z
dc.date.available2019-09-23T16:43:39Z
dc.date.issued2019-09-16
dc.description.abstractIn this thesis we obtain angular displacements using two different approaches to improve sensor robustness to magnetic and shock disturbances; also, we discuss the pros and cons of these two different approaches. The first approach is the supervised learning filter (SLF) approach, and the second is the supervised learning-Kalman filter (SL-KF) approach. In SLF, azimuth angle errors obtained from different sensors (magnetometers, accelerometers, and gyroscopes) are compared under magnetic and shock disturbance conditions; then, we employ an adaptive neuro fuzzy inference system (ANFIS) to calculate the error models of the sensors. Based on these sensors’ error models, the proper weights of the azimuth angles obtained from different sensors are computed and applied to the azimuth angles to output a final azimuth angle. However, to achieve the best results of SLF, we assume that at least one magnetometer is not affected by interferences at the same time interval (two magnetometers are separated by a distance D, and D can prevent both magnetometers from being affected by a magnetic disturbance at the same time). Therefore, SL-KF combines SLF with a KF to further reduce the effect of disturbances on sensors. SLF computes the corrected rotational angles and angular velocities that are subsequently fed into a global filter KF, which performs further corrections. The present subsurface positioning (directional drilling) relies on angular displacements and values of measurement depth (drill string length) to estimate a well path. However, these methods have limitations to apply in working conditions (for example drill string length maybe inaccurate caused by steel expands with increased temperature and stress). To deal with the drill string length inaccuracy problem, instead of using real external measurement signals (drill string length), we use correction signals designed based on the dual acceleration difference (DAD) method to correct the positions.en_US
dc.identifier.citationLiu, H. (2019). Subsurface Sensing Through Data Fusion of Redundant IMU Sensors with Supervised Learning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/37099
dc.identifier.urihttp://hdl.handle.net/1880/111037
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectSupervised Learningen_US
dc.subjectDirectional Drillingen_US
dc.subjectMWDen_US
dc.subjectANFISen_US
dc.subjectMagnetic Disturbance Robusten_US
dc.subjectShock Impact Robusten_US
dc.subjectSubsurface Sensingen_US
dc.subjectDual Acceleration Difference Methoden_US
dc.subjectKalman Filteren_US
dc.subject.classificationApplied Mechanicsen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationEngineering--Mechanicalen_US
dc.titleSubsurface Sensing Through Data Fusion of Redundant IMU Sensors with Supervised Learningen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Mechanical & Manufacturingen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
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