Distributed Acoustic Sensor Data Downsampling with Noise Interpolation
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Solution Overview
Problem
Distributed acoustic sensing (DAS) data collected during hydrocarbon recovery operations, such as well drilling, often suffers from noise that corrupts spatial consistency across channels, making it difficult to accurately downsample and interpret seismic measurements.
Innovation Solution
The method involves identifying and removing noisy data channels, interpolating them from surrounding data, and applying filters based on a priori information and the expected velocity of elastic waves to maintain spatial consistency and improve the signal-to-noise ratio, thereby enabling accurate downsampling of DAS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DAS data is downsampled to reduce data volume, then data processing efficiency is improved, but noise corruption makes it difficult to maintain spatial consistency and measurement accuracy
Solution Approach 1:
The patent applies preliminary denoising and spatial consistency correction to the DAS data before downsampling. By preprocessing the data to remove noise and correct spatial inconsistencies in advance, the method ensures that the subsequent downsampling operation maintains measurement accuracy and spatial consistency, thus resolving the contradiction between processing efficiency and measurement precision.
2Measurement precision
If noise correction is applied to maintain spatial consistency, then measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent transforms the DAS data into the frequency-wavenumber (f-k) domain, where noise and signal can be separated based on their different frequency and wavenumber characteristics. By applying filtering operations in this transformed domain and then inverse transforming back, the method effectively removes noise and maintains spatial consistency while managing processing complexity through efficient spectral domain operations.
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
This approach results in higher-quality downsampled data with improved signal-to-noise ratio, allowing for more accurate detection of acoustic events and reducing aliasing distortion, applicable to various well types and formations.
Implementation Method 1
This sensing is performed by interrogating the backscattered light returning from the waveguide
Data Source
AI summary
In accordance with embodiments of the present disclosure, systems and methods for downsampling DAS data in a way that enables accurate interpretation of acoustic events occurring in the data are provided. Such methods may be particularly useful when interpreting large sets of data, such as DAS VSP data collected during hydrocarbon recovery operations. The methods generally involve identifying data channels affected by noise from a DAS data set, and then interpolating from the surrounding data. This may improve the quality of the resulting downsampled data, with respect to the signal to noise ratio, compared to what would have occurred by merely decimating unwanted data channels. In addition, a priori information about channel fading, the desired downsampling rate, and the slowest expected elastic waves may be used to filter the DAS data. This may achieve a higher signal-to-noise ratio in the downsampled data.