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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
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.
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.
Keywords:
data compressiondeep autoencoderlogging while drilling (LWD)well trajectorywellbore safety monitoringMore Related Videos
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