Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data
Xin Feng1, Qiang Feng2, Shaohui Li3
1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|August 23, 2020
Summary
This study introduces a wavelet-based Kalman smoothing method to improve oil well-testing data processing. The new approach enhances data accuracy and robustness for better oil and gas field development decisions.
Area of Science:
- Geophysics and Petroleum Engineering
- Signal Processing and Data Analysis
Background:
- Classical well-testing data processing methods struggle with data stochasticity, parameter randomness, and poor denoising capabilities.
- These limitations hinder real-time prediction of oil operation status and offline interpretation of historical data.
Purpose of the Study:
- To develop an advanced method for processing uncertain well-testing data in oil and gas fields.
- To enhance the accuracy, robustness, and real-time predictability of oil well-testing data analysis.
Main Methods:
- A wavelet-based Kalman smoothing method was developed, optimizing wavelet decomposition scale and vanishing moments for oil data.
- A ground pressure measuring platform was utilized for online data processing, including wavelet decomposition, filtering, and Kalman prediction.
- Particle swarm optimization was employed to determine optimal Kalman parameters for data smoothing.
Main Results:
- The proposed method demonstrated superior performance in terms of signal-to-noise ratio and reduced root mean square error compared to classical methods.
- The method effectively filters noise and interference, reduces reconstruction error, and provides high-resolution, robust data.
- Experimental results confirmed the method's decorrelation, data compression, and minimal variance unbiased estimation capabilities.
Conclusions:
- The wavelet-based Kalman smoothing method offers significant improvements for processing uncertain well-testing data.
- The technique provides technical support for real-time data uploading and enhances the reliability of oil and gas field development decisions.
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