Adaptive Tubing Data Filtering for Defect Detection
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Solution Overview
Problem
Conventional instruments for evaluating tubing in oil wells face challenges such as inflexible signal processing, noise misinterpretation, and blurring of defects due to varying tubing speeds, leading to inaccurate characterization of tubing conditions.
Innovation Solution
A digital signal processing system that applies adaptive filtering and averaging to tubing data, adjusting the processing level based on tubing speed and noise, to enhance data fidelity and preserve signal structures indicative of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional fixed filtering is applied to tubing data, then signal stability is improved, but measurement precision deteriorates due to noise misinterpretation and defect blurring
Solution Approach 1:
The patent applies adaptive filtering that dynamically adjusts filter strength based on tubing speed variations. The filter transitions from strong filtering at low speeds to weak filtering at high speeds, optimizing the balance between noise suppression and defect preservation for each operating condition
Solution Approach 2:
The system changes the filtering parameter (filter strength) based on the tubing speed parameter. When tubing speed exceeds a threshold, the filter strength is reduced to preserve defect signals; when speed is below the threshold, full filtering is applied to suppress noise
2Stability of the object's composition
If strong filtering is applied to smooth noise, then signal stability is improved, but defect signals are blurred and lost
Solution Approach 1:
The filtering strength is made dynamic rather than fixed. The system automatically reduces filter strength when tubing speed increases, preventing defect signal blurring during high-speed operation while maintaining strong noise suppression during low-speed operation
Solution Approach 2:
The system uses tubing speed as feedback to adjust filtering strength. The speed signal feeds back to the filtering module, creating a closed-loop system that adapts filtering parameters based on real-time operating conditions to preserve defect information
3Device complexity
If fixed-time sampling is used for data acquisition, then system simplicity is maintained, but measurement precision deteriorates due to speed-induced sample spacing variations
Solution Approach 1:
The sampling parameter (sampling rate) is changed based on tubing speed. The system increases sampling rate when tubing speed increases and decreases sampling rate when speed decreases, maintaining consistent sample spacing along the tubing length for accurate defect characterization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides more accurate, precise, and efficient evaluation of tubing conditions by smoothing noise while maintaining the integrity of defect signals, regardless of tubing speed fluctuations.
Implementation Method 1
The instrument may create a magnetic field into which the tubing is disposed, and the transducer may detect changes or perturbations in the field resulting from the presence of the tubing and any anomalies of that tubing
Data Source
AI summary
An instrument, such as a wall-thickness, rod-wear, or pitting sensor, can monitor tubing as a field service crew extracts the tubing from an oil well or inserts the tubing into the well. A digital system can process data from the instrument to improve the data's fidelity, quality, or usefulness. Digital signal processing can comprise filtering or otherwise manipulating the data to provide refined data that a person or machine can readily interpret. For example, a graphical representation of the refined data can help an operator evaluate whether a segment of tubing is fit for continued service. Processing tubing data can comprise applying a flexible level of filtering, smoothing, or averaging to the data, wherein the level changes based on a criterion or according to a rule. The level can vary in response to a change in tubing speed, noise in the raw data, or some other parameter.