Research on comprehensive and effective acoustic signal processing methods for caculating downhole liquid level depth

Wang, Luping; Yong, Wei; Wang, Yuxiang; Chen, Qiang; Liu, Ping; Chai, Xiaofei

doi:10.1016/j.measurement.2022.111452

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Based on engineering experience from oil extraction wells, the depth of the dynamic liquid level ranges from 500 to 3000 m, the sound speed in oil extraction wells is between 320 m/s and 360 m/s and the signal sampling rate is between 250 Hz and 1 kHz [ 32 , 33 ]. The single infrasound wave acquisition time for the dynamic liquid level monitoring system is 90 s, during which data are collected once every 4 milliseconds, resulting in a total of 22,500 data points.…”

Section: Design and Implementationmentioning

confidence: 99%

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Liu,

Fan,

Liu

et al. 2024

Sensors

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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.

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Section: Design and Implementationmentioning

confidence: 99%

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Liu,

Fan,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In view of the influence of the complex structure in the casing on the echo method, Zhou et al (2018) have argued a spectral fast Fourier transform based on the Welch power spectrum to obtain the resonance frequency difference, obtain the real resonance frequency, and calculate the oil well dynamic liquid level. Wang et al (2021) have argued the Butterworth low-pass filtering with butt hoop wave and then used the wavelet singular value detection method to detect the dynamic liquid-level depth of oil well. The above methods improve the measurement inaccuracy caused by many factors, but there are still problems of high error and long measurement cycles.…”

Section: Introductionmentioning

confidence: 99%

Multi-well dynamic liquid-level prediction method of pumping well based on dynamic and static information feature fusion neural network

Ming-xing

Leng

2022

Transactions of the Institute of Measurement and Control

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In a rod pumping system, accurate prediction of dynamic liquid level is a key to rational optimization of pumping system parameters. At present, it is difficult to use a uniform prediction model for dynamic liquid-level prediction in different rod pumping systems. To this end, a method for predicting the dynamic liquid level of multiple wells in a pumping well based on dynamic and static information feature fusion (DSIFF) neural network is proposed in this paper. According to the principle of dynamic liquid-level calculation, suspension displacement, suspension load, pumping rod parameters, formation crude oil density, surface crude oil density, oil pressure, and casing pressure are used as inputs. The subnetwork feature extraction method is proposed for the problem of large network structure caused by the high dimension of the important dynamic data of suspension displacement and suspension load. The Huber loss function is used as the loss function of the prediction model for the case of abnormal data in the large amount of data required for model training. Finally, the results of comparative analysis show that the proposed method solves the problem of unified establishment of dynamic liquid-level prediction model for multiple oil wells and has better robustness for abnormal data.

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Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information

Leng,

Jia,

Zheng

et al. 2023

Energy

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Research on comprehensive and effective acoustic signal processing methods for caculating downhole liquid level depth

Cited by 7 publications

References 21 publications

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Multi-well dynamic liquid-level prediction method of pumping well based on dynamic and static information feature fusion neural network

Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information

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