Bi-orthonormal based De-Noising for Improving Wellbore Continuous MWD Surveying Utilizing MEMS Inertial Sensors
Umar Iqbal, Mississippi State University, USA, Lu Wang, Beijing University of aeronautics and astronautics, China; Abdalla M. Osman,Royal Military College of Canada; Aboelmagd Noureldin, Queen’s University and Royal Military College of Canada, Chunxi Zhang, Beijing University of aeronautics and astronautics, China;
Location: Big Sur
Directional drilling is the technology of directing a wellbore along a predefined trajectory leading to a subsurface target. Complete knowledge of the drill bit position and orientation downhole during the drilling process is essential to guarantee efficient directional drilling process. Thus, besides the conventional drilling assembly, directional drilling operations require positioning and orientation sensors to provide estimations of the heading angle (azimuth angle), the inclination (pitch angle), and the toolface angle (roll angle) of the drill bit. The positioning and orientation sensors assembly is a crucial part of the Measurement While Drilling system (MWD) equipment.
The traditional MWD system utilizes magnetometers and accelerometers to provide the position and orientation information about the well trajectory. However, the magnetometers measurements are influenced by disturbing magnetic field in the drilling hole. Therefore, replacing the magnetometers in MWD with the gyroscope technology has been a tendency [1,2]. Due to the space limitation of bottom hole assembly, the MWD tool cannot accommodate a full inertial navigation system. Thus, the reduced inertial sensor system (RISS) for MWD has been proposed in this paper based on Micro-Electro-Mechanical System (MEMS) based inertial sensors . Compared to the full inertial navigation system with three gyros and three accelerometers, the RISS is a 3D navigation solution with single-axis MEMS gyro and three-axis MEMS accelerometers. Utilizing the measurement of the single gyro, we can obtain the azimuth angle. The inclination and the toolface angle are derived from the measurements of the three-axis accelerometers together with an accurate earth gravity model.
MEMS inertial sensors have several advantages that are suitable for MWD including low-cost, small-size, light-weight, and low-power consumption. However, the measurements of the MEMS inertial sensors suffer from bias instabilities and high levels of noise. While drilling the hole, the MWD is working in the harsh environment with strong shock and vibration which leads to more disturbances in the sensors’ measurements . In order to improve the accuracy of estimating the position, azimtuh, it is critical to reduce the noise level prior to process the sensors measurements using RISS method.
Several techniques have been reported to eliminate the noise and other disturbances in the inertial sensors, such as the wavelets transform, neural network techniques. The existing adaptive signal processing techniques have been used successfully to reduce the noise level in a variety of applications predominantly related to communication channel equalization, speech recognition and video sequence restoration . However, conventional signal de-noising methods are mainly based on linear combination of orthonormal and bi-orthonormal basis. This imposes a limitation on signal de-noising accuracy as the basis used to represent the signal is required to satisfy the orthogonality or bi-orthogonality criterion . Alternatively, using non-orthogonal representation eliminates the limitation on the basis elements and improves signal de-noising accuracy. The main drawback of non-orthogonal de-noising method is the complexity of calculating its coefficients . This study aims at employing adaptive signal processing techniques to reduce the noise component at the output of inertial sensors to enhance the wellbore surveying performance. A new de-noising method named Bi-orthonormal Optimal Smart Search (BIOSS) was proposed. This new signal de-noising method is based on optimal bi-orthonormal signal approximation. It employs Euclidean distance norm as a selection criterion to determine candidate function (basis) that constitute the represented signal from over-complete dictionary and compute their corresponding coefficients. The calculation efficiency of BIOSS was further enhanced by derivation of the term selection formula to avoid multiple matrix calculations and reduce the processing time of BIOSS.
The MWD surveying data sets are obtained in a laboratory environment from the Crossbow MEMS Inertial Measurement Unit (IMU300CC), which is mounted on three-axis positioning and rate table. The Inertial sensors measurements are de-noised by BIOSS with the performance being compared to the conventional Wavelet-based de-nosing. We use Allan variance analysis to identify the bias drift, random walk and other statistical characteristics of the de-noised data. The de-noised inertial sensor measurements are processed through the RISS algorithm together with an extended Kalman filtering module for the update measurements corresponding to the length of the drill pipe and the penetration rate provided in oil drilling fields. The experiment results showed that the proposed method can significantly improve the computation of the position and attitude of the bottom hole assembly while using de-noised measurements from low cost MEMS based inertial sesors processed through the 3D RISS algorithm utilized for the first time for MWD applications.
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