Liu Zhiyang, Fan Qi, Liu Jianjian, Zhou Luoyu, Zhang Zhengbing
School of Electronics Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Institute for Artificial Intelligence, Yangtze University, Jingzhou 434023, China.
Sensors (Basel). 2024 Jun 3;24(11):3607. doi: 10.3390/s24113607.
Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.
油井动态液位监测与测量对于确保采油机械的安全高效运行以及制定合理的开采策略以提高油田产量至关重要。本文提出了一种基于智能次声的油井动态液位测量方法；旨在解决传统测量方法存在的高成本、低精度、低鲁棒性和实时性不足等挑战。首先，引入了一种新颖的降噪算法，有效减轻周期性和随机噪声，从而显著提高动态液位检测的准确性。此外，利用PyQT框架设计了一个实时动态液位监测软件平台，能够生成液位剖面图、计算声速和液深并可视化监测数据。为增强数据存储和分析能力，该系统采用全天候无人值守监测方法，利用物联网（IoT）技术，通过LoRa和4G通信模块将采集到的动态液位数据和计算结果传输到油田的中央数据存储库。油井动态液位监测与测量的现场试验表明，测量范围为600米至3000米，结果一致且可靠，满足油井动态液位监测与测量的要求。这一创新系统为动态液位深度的计算和监测提供了新的视角和方法。
