Strategies in High-Data-Rate MWD Mud Pulse Telemetry

Su, Yinao; Sheng, Limin; Li, Lin; Bian, Hailong; Shi, Rong; Zhuang, Xiaoying; Chin, Wilson C.

doi:10.7569/jsee.2014.629515

Cited by 9 publications

(1 citation statement)

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“…Therefore, increased vibration and shock loads on the drilling tools can influence the reliability of sensors leading to outliers [ 53 ]. Subsequently, real-time data transmission from downhole sensors due to signal interference can corrupt the data, resulting in outliers, where the longer the drill strings, the more signal attenuation and data transmission delays, or potential signal loss in the received data [ 54 ]. The influence of operational worker errors in data acquisition and recording also is another potential source of recorded outliers [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

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

Abbaszadeh Shahri,

Shan,

Larsson

et al. 2024

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%