Low Frequency Distributed Acoustic Sensing for Hydraulic Fracturing
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Solution Overview
Problem
Current visualization and interpretation of Distributed Acoustic Sensing (DAS) data primarily focus on high-frequency signals, leading to the removal of lower frequency and ultra-low frequency signals, which are crucial for understanding strain-field responses and hydraulic fracturing processes in hydrocarbon production.
Innovation Solution
A process involving the installation of fiber optic cables along hydrocarbon wellbores, interrogating them to obtain DAS datasets, and transforming these datasets using low-pass filtering and down-sampling to 1-50 milliHz to generate a continuous DAS record, allowing for the interpretation and monitoring of hydrocarbon production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency DAS signals are used for analysis, then useful information for proppant allocation estimation and stimulation operation monitoring is obtained, but lower frequency and ultra-low frequency signals are removed from the data
Solution Approach 1:
The patent segments the DAS signal analysis into different frequency bands: high-frequency (>1 Hz) for proppant allocation and stimulation monitoring, and ultra-low frequency (0-50 mHz) for strain-field response and hydraulic fracturing process understanding. This segmentation allows each frequency band to be analyzed independently for its specific applications without interference.
Solution Approach 2:
The patent extends the traditional single-frequency DAS analysis by adding a temporal dimension through ultra-low frequency monitoring. By transforming the continuous DAS record into ultra-low frequency signals through low-pass filtering and down-sampling, the system captures slow-varying strain-field responses that complement the high-frequency acoustic signals.
2Loss of information
If continuous high-frequency DAS data is collected, then detailed acoustic information is captured, but data volume and processing complexity increase
Solution Approach 1:
The patent extracts only the relevant ultra-low frequency components from the continuous high-frequency DAS data through low-pass filtering. This extraction process removes the high-frequency acoustic signals while retaining the slow-varying strain-field responses, significantly reducing data volume and processing requirements for applications that only need ultra-low frequency information.
Solution Approach 2:
The patent applies partial action by selectively processing only the frequency components needed for specific applications. For ultra-low frequency analysis, only the 0-50 mHz band is processed through down-sampling and filtering, rather than analyzing the entire high-frequency spectrum, thereby reducing computational complexity while maintaining necessary measurement precision.
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 enhances the signal-to-noise ratio and recovers the polarity of strain-rate measurements, providing detailed insights into hydraulic fracturing processes, such as stage isolation, fracture geometry, and stimulated reservoir volume, thereby improving completion operations and production monitoring.
Implementation Method 1
DAS is the measure of Rayleigh scatter distributed along the fiber optic cable. A coherent laser pulse is sent along the optic fiber, and scattering sites within the fiber cause the fiber to act as a distributed interferometer
Implementation Method 2
This type of system is very sensitive to both strain and temperature variations of the fiber and measurements can be made almost simultaneously at all sections of the fiber
Data Source
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AI summary
The invention relates to DAS observation has been proven to be useful for monitoring hydraulic fracturing operations. While published literature has shown focus on the high-frequency components (> 1Hz) of the data, this invention discloses that much of the usable information may reside in the very low frequency band (0-50 milliHz). Due to the large volume of a DAS dataset, an efficient workflow has been developed to process the data by utilizing the parallel computing and the data storage. The processing approach enhances the signal while decreases the data size by 10000 times, thereby enabling easier consumption by other multi-disciplinary groups for further analysis and interpretation. The polarity changes as seen from the high signal to noise ratio (SNR) low frequency DAS images are currently being utilized for interpretation of completions efficiency monitoring in hydraulically stimulated wells.