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A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

Shan Song1, Xiaoyong Zhao2, Zhengbing Zhang1

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
Summary

A novel deep autoencoder compression method significantly improves wellbore trajectory data compression ratios and reduces errors. This enhances wellbore stability monitoring by overcoming limitations of traditional methods.

Keywords:
data compressiondeep autoencoderlogging while drilling (LWD)well trajectorywellbore safety monitoring

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Area of Science:

  • Petroleum Engineering
  • Data Science
  • Signal Processing

Background:

  • Wellbore trajectory data compression is vital for effective wellbore stability monitoring.
  • Classical methods (Huffman coding, compressed sensing, DPCM) exhibit limitations in real-time performance, compression ratio, and data reconstruction accuracy.

Purpose of the Study:

  • To develop an advanced compression method for wellbore trajectory data.
  • To improve compression ratios and minimize reconstruction errors compared to existing techniques.

Main Methods:

  • Utilizing a deep autoencoder for primary data compression.
  • Employing quantization and Huffman coding for residual data compression.
  • Applying a mean filter with an optimal standard deviation threshold for error reduction.

Main Results:

  • Achieved an average compression ratio of 4.05 for inclination and azimuth data, an 118.54% improvement over DPCM.
  • Reduced average mean square error to 76.88, an 82.46% decrease compared to DPCM.
  • Ablation studies validated the effectiveness of individual components of the proposed method.

Conclusions:

  • The proposed deep autoencoder-based method offers superior performance for wellbore trajectory data compression.
  • This advancement significantly enhances the accuracy and efficiency of wellbore stability monitoring.