Decoding Fluid Flow Characteristics Through Distributed Acoustic Sensing: A Novel Approach

利用分布式声学传感解码流体流动特性:一种新方法

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Abstract

Flow characteristic monitoring includes parameters such as flow regime, fluid characteristic frequency, and flow rate, which are crucial for optimizing production and ensuring the safety of oil and gas transportation systems. Existing fluid monitoring technologies, such as various flow meters, often face limitations in providing distributed and real-time monitoring data. In contrast, distributed acoustic sensing offers a spatial resolution of 1 m with high frequency sampling capability, allowing for long-term, multi-point dynamic monitoring of fluid migration characteristics. We developed an indoor physical simulation pipeline loop to assess the feasibility of using distributed acoustic sensing for monitoring flow migration characteristics. The experiment collected signal characteristics under different conditions, including background noise, single gas-phase flow, single liquid-phase flow, and gas-liquid two-phase flow. In the frequency-power spectral density analysis, single gas-phase flow signals are concentrated at lower frequencies, single liquid-phase flow displays noticeable spikes over a broader frequency range, and gas-liquid two-phase flow covers the widest frequency range with stronger amplitude signals. Autocorrelation analysis shows larger oscillations for gas-liquid two-phase flow, smoother signals for gas-phase flow, and more turbulent signals for liquid-phase flow. By examining root mean square energy changes, flow rates can be qualitatively estimated, revealing a positive correlation between energy and flow velocity. Finally, the study discussed the limitations of the experimental setup and proposed improvements and advanced research directions of distributed acoustic sensing in fluid monitoring.

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