Well DAS Coupling Quality Qualification Using Amplitude Spectra
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Solution Overview
Problem
The quality of distributed acoustic sensing (DAS) data is often compromised by poor coupling of the DAS sensing element to the well structure, leading to artifacts that affect the signal-to-noise ratio and reduce the reliability of data for downstream applications in well operations.
Innovation Solution
A method involving data qualification and remediation processes is employed to identify and address poor coupling issues in DAS data by analyzing frequency domain data, performing clustering and cross-correlation analysis, and providing real-time feedback to improve coupling quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If DAS sensing element is positioned in the well to obtain measurement data, then data coverage is improved, but acoustic coupling artifacts are introduced that reduce data quality
Solution Approach 1:
The patent performs a qualification process before downstream use of DAS data to identify poor acoustic coupling conditions. By detecting artifacts in advance using clustering analysis and cross-correlation coefficients, the system prevents unreliable data from being used in well modeling and operation planning, thus maintaining data quality while preserving comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary qualification process that acts as a filter between data acquisition and downstream applications. This intermediary layer uses template matching and statistical analysis to identify and flag artifacts, allowing the system to maintain both comprehensive data collection and high data quality by selectively processing only reliable portions.
2Measurement precision
If DAS sensing element is acoustically coupled to well structure, then measurement sensitivity is improved, but artifacts are introduced that reduce signal-to-noise ratio
Solution Approach 1:
The patent converts the harmful effect of acoustic coupling artifacts into a beneficial diagnostic tool. By analyzing the characteristic patterns of artifacts through clustering analysis and cross-correlation with templates, the system identifies and flags poor coupling conditions, transforming what was previously harmful noise into useful information for quality control.
Solution Approach 2:
The patent changes the parameter of data quality assessment by moving from raw amplitude measurements to statistical characterizations such as cross-correlation coefficients and clustering metrics. This parameter transformation allows the system to distinguish between legitimate acoustic signals and coupling artifacts, maintaining sensitivity while filtering out harmful effects.
3Reliability
If qualification process is performed on DAS data, then data reliability is improved, but processing time and complexity increase
Solution Approach 1:
The patent segments the DAS data into multiple portions and processes each segment independently through clustering analysis. By dividing the data processing into manageable portions rather than analyzing the entire dataset at once, the system maintains high data reliability through thorough qualification while reducing overall processing complexity and making the workflow more tractable.
Data Source
AI summary
Methods and systems for managing operation of a well are disclosed. The method may include obtaining distributed acoustic sensing (DAS) data based on a measurement made using a DAS sensing element positioned in the well. A qualification process for the DAS data may be performed to identify whether any portion of the DAS sensing element was acoustically coupled to a structure of the well during the measurement in a manner that introduced at least one artifact to the DAS data. If at least one portion of the DAS sensing element was coupled in such a manner, a remediation process may be performed to manage impacts of the at least one artifact on downstream use of the DAS data.