Low-Frequency DAS SNR Improvement via Signal Processing
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Solution Overview
Problem
Low-Frequency Distributed Acoustic Sensing (DAS) in well logging faces challenges with signal-to-noise ratio (SNR) issues due to spike noise, low-frequency drift, vibrations, and thermal recoupling, leading to inaccurate data and increased analysis time, which hinders decision-making in oil and gas production optimization.
Innovation Solution
A workflow that applies signal processing techniques such as 2D median filtering, drift removal, FK-filtering, thermal recoupling, and automatic gain control to enhance the SNR of low-frequency DAS measurements, mitigating noise sources and improving data quality for better analysis and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-frequency DAS measurements are used for well logging, then spatial and temporal resolution is improved, but signal-to-noise ratio deteriorates due to spike noise, low-frequency drift, vibrations, and thermal recoupling
Solution Approach 1:
The patent applies 2D median filtering that segments the DAS signal in both spatial and temporal dimensions, processing the data in a two-dimensional grid to separate signal from noise. This segmentation approach allows the filter to identify and remove spike noise while preserving the underlying low-frequency DAS signal, thereby improving SNR while maintaining the high resolution measurements.
Solution Approach 2:
The patent extracts and removes specific noise components from the DAS signal through drift removal techniques that isolate low-frequency drift from the measurement signal. By separating and eliminating the drift component, the method preserves the high spatial and temporal resolution of the original measurements while improving the signal-to-noise ratio.
Solution Approach 3:
The patent applies FK-filtering that transforms the DAS data from the time-space domain to the frequency-wavenumber domain, where noise and signal can be separated based on their different propagation characteristics. By changing the parameter domain and applying selective filtering, the method enhances SNR while maintaining the high resolution capabilities of low-frequency DAS.
Solution Approach 4:
The patent employs a composite signal processing workflow that combines multiple techniques (2D median filtering, drift removal, FK-filtering, thermal recoupling correction, and automatic gain control). This composite approach addresses multiple noise sources simultaneously, improving overall SNR while preserving the high spatial and temporal resolution of the low-frequency DAS measurements.
2Reliability
If signal processing techniques are applied to improve SNR, then data quality is improved, but analysis time increases
Solution Approach 1:
The patent applies 2D median filtering and drift removal as preliminary processing steps that prepare the DAS data before further analysis. By removing dominant noise components early in the processing workflow, subsequent analysis steps require less computational effort and time, offsetting the initial processing time investment with reduced downstream analysis requirements.
Solution Approach 2:
The patent employs FK-filtering that adaptively processes the DAS data based on the frequency and wavenumber characteristics of the signal and noise. This dynamic filtering approach efficiently targets noise components without requiring excessive computational resources, improving data quality while maintaining reasonable processing times.
Solution Approach 3:
The patent applies automatic gain control that automatically adjusts the gain of the DAS signal based on the local noise level and signal strength. This self-adjusting technique improves data quality across varying signal conditions without requiring manual intervention or complex computational optimization, thereby improving data quality efficiently.
3Reliability
If multiple noise sources are removed using various filtering techniques, then signal-to-noise ratio is improved, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional processing workflow where each technique (2D median filtering, drift removal, FK-filtering, thermal recoupling correction, and automatic gain control) addresses multiple noise sources simultaneously. For example, the 2D median filter handles both spike noise and some vibration noise, while FK-filtering addresses both vibration and thermal recoupling artifacts. This multi-functionality reduces the need for separate specialized processors for each noise type.
Solution Approach 2:
The patent transforms the DAS data into the frequency-wavenumber domain using FK-filtering, where multiple types of noise (vibrations, thermal recoupling) have distinct characteristic patterns that can be separated and removed through parameter-based filtering. This parameter change approach allows a single filtering operation to address multiple noise sources that would otherwise require separate processing steps in the time domain.
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 described workflow significantly improves the signal-to-noise ratio of low-frequency DAS data, facilitating more accurate analysis, reducing costs, and enabling better integration with other techniques for enhanced production optimization and reservoir management.
Implementation Method 1
DAS is the measure of Rayleigh scatter distributed along the fiber optic cable
Implementation Method 2
an optical time-domain (e.g. optical time-domain reflectometry (OTDR))
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A workflow for optimizing production of a hydrocarbon reservoir and optimizing a hydraulic fracturing model of a hydrocarbon reservoir using techniques for improving signal-to-noise ratio and decreasing interferences for Low-Frequency Distributed Acoustic Sensing is described. Acoustic sensor data is collected from optical fiber, where said data is processed to optimize production parameters and well production.