Novel two-step filtering scheme for a logging-while-drilling system

Zhao, Qingjie; Zhang, Baojun; Hu, Huosheng

doi:10.1016/j.cpc.2009.04.011

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Therefore, identifying and handling outliers in MWD data is crucial for maintaining the accuracy of drilling operations and making informed decisions. Mathematically, the MWD data can be filtered using different techniques such as bandpass [ 36 ], moving average [ 37 ], Kalman [ 38 ], and wavelet [ 39 ]. However, the choice of filtering technique has a close dependency on the specific application and the characteristics of the MWD data being analyzed [ 40 ].…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…This method used center-ofgravity technique to obtain mud pulse positions of downhole signal modulated by pulse positioning modulation (PPM) scheme. Later Zhao et al (2009) used the average filtering algorithm to decay DC noise components and a windowed limited impulse response (FIR) algorithm deployed to filter high frequency noise. Yuan and Gong (2011) studied the use of directional difference filter and band-pass filter methods to remove noise on the continuous mud pulse differential binary phase shift keying (DBPSK) modulated downhole signal.…”

Section: Adaptive Noise-filtering Techniquesmentioning

confidence: 99%

A review of mud pulse telemetry signal impairments modeling and suppression methods

Mwachaka

2018

J Petrol Explor Prod Technol

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Exploration and drilling operations are the most costly and risky activities that require logging while drilling effective tool operation and high speed real-time data transmission. Among other logging technologies, mud pulse telemetry has been widely adopted into real-time drilling and logging data measurements for deep formation evaluation decision-making. However, the increased downhole information quantity and quality requirements have elevated demands for greater bandwidth and higher data rate transmission. Maximum possible mud pulse telemetry data rates are obstructed by unpredictable signal impairments. Researchers have implemented numerous downhole signal modulation techniques, data compression algorithms and surface receiver noise cancellation techniques to lower bit errors and increase signal-to-noise ratio. This study reviews mud pulse telemetry system signal transmission challenges and current countermeasure trends. Rigorous technical literature reviews from exploration and drilling oilfield service industry have been at the receiving end debated. Mud channel signal characteristics, signal attenuation factors, signal detection and decoding techniques are critical to successful mud pulse operations. To minimize exploration, drilling and economic risks, this study explores mud pulse data transmission challenges, strategies and possible technical solutions to the next generation engineers and scholars in the field.

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“…Firstly, a dynamic part mean filtering algorithm is proposed to separate the direct current components, and then a limited impulse response filtering algorithm is deployed to filter out the high-frequency noise. We have provided detailed implementations of these algorithms in [9].…”

Section: Signal Filteringmentioning

confidence: 99%

“…In [7] and [8], a limited impulse response low-pass filter is used as a DC (direct current) estimator, and a band-pass filter is used to eliminate the large out-of-band noise components caused by the mud pumps, and at last a zero mean signal is acquired. In [9], we propose a two-step filtering method in which a dynamic part mean filtering algorithm is proposed to separate the direct current components and a windowed limited impulse response algorithm is used to filter out the high-frequency noise.…”

Section: Introductionmentioning

confidence: 99%

Data processing techniques for a wireless data transmission application via mud

Zhao

Zhang

Wang

2011

EURASIP J. Adv. Signal Process.

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The data measured by well bottom sensors can be transmitted to the surface through the drilling mud during oil drilling operations. This article introduces a data processing scheme for a wireless data transmission application via mud. The detailed signal processing procedure is given, and several data processing techniques used are discussed, mainly including data encoding and signal integrating method, signal filtering, data storage and manage method, peak detection, signal recognition, and data decoding method. The article uses M pulses in N slots to encode the values of actual parameters. A two step filtering method and a dynamic data storing and managing method are proposed. A mix peak detection method is utilized to find the position of a pulse by combining threshold method and neighbor comparison method. These techniques have been successfully used in an oil well drilling operation.

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Novel two-step filtering scheme for a logging-while-drilling system

Cited by 8 publications

References 4 publications

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

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

A review of mud pulse telemetry signal impairments modeling and suppression methods

Data processing techniques for a wireless data transmission application via mud

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