Bi-orthonormal based de-noising for improving wellbore continuous MWD surveying utilizing MEMS inertial sensors

Abstract

Typically, magnetic based Measurement-while-drilling (MWD) instruments are utilized to provide monitoring of the position and orientation for the horizontal drilling processes in the oil industry. Inertial Measurement Unit (IMU) based on low cost Micro-Electro-Mechanical System (MEMS) for MWD system offers a number of advantages such as low power consumption, immunity to shock and vibration. However, their precision degrades significantly in short span of time due to residual random errors of the inertial sensors. This paper introduces a novel Bi-orthonormal Optimal Signal Search (BIOSS) based de-noising for improving wellbore continuous MWD surveying utilizing MEMS inertial sensors. This new signal de-noising method is based on optimal bi-orthonormal signal approximation. The simulated MWD data sets are obtained in a laboratory environment by MEMS IMU, 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's measurements are processed through the reduced inertial sensor system (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 shown that the proposed method can significantly improve the computation of the position and attitude of the bottom-hole-assembly (BHA) while using de-noised measurements from low cost MEMS based inertial sensors processed through the 3D RISS algorithm utilized for the first time for MWD applications.