Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering.

A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment.

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.