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Intelligent detection and suppression methodology for noise interference of oil well pumping units in seismic data processing |
ZHANG Meng( ) |
Geophysical Research Institute,Shengli Oilfield Company,SINOPEC,Dongying 257022,China |
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Abstract Noise detection and suppression of oil well pumping units pose challenges in data processing for mature exploration areas.The conventional method in the industry is to identify pumping unit noise through manual interactions and then suppress it as high-amplitude interference.However,manual identification wastes manpower and yields low detection accuracy,often resulting in missed detections.Hence,based on the noise characteristics of pumping units,this study conducted noise detection on seismic data containing pumping unit noise using deep learning methods.It then estimated the bandwidth of the detected noise using mathematical morphology techniques to determine the final position and distribution pattern of the noise.This allows for adaptive parameter support for the anomalous amplitude attenuation(AAA) method to achieve automatic detection and efficient suppression of pumping unit noise.The processing results of actual seismic data reveal that the methodology used in this study enables intelligent detection of pumping unit noise,significantly reducing the manual effort required for noise identification,improving the detection accuracy,and enhancing the fidelity and robustness of the AAA method.
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Received: 30 September 2024
Published: 22 April 2025
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Specific manifestation of well-pump noise in the work area
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Raw single-shot record with well-pump noise
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Spectrum of raw single-shot record with well-pump noise a—spectrum of the useful signal;b—spectrum of the well-pump noise
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Used FCN Fnetwork structure
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Schematic diagram of well-pump noise detection process based on deep learning
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Schematic diagram of well-pump noise detection and suppression process based on deep learning
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Schematic diagram of outsource perturbation suppression process based on deep learning
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Loss function values for network training(a) and validation(b)
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Well-pump noise detection results
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Field seismic data with well-pump noise and erratic high-amplitude interference
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Results(a) and noise(b) obtained solely using the AAA method
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Results(a) and noise(b) obtained by the proposed method
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[1] |
吴龙丽. 压制抽油机噪音的三维矢量组合法[J]. 油气地质与采收率, 2010, 17(6):51-53,114.
|
[1] |
Wu L L. Three-dimensional vector combination method for suppressing the noise of pumping unit[J]. Petroleum Geology and Recovery Efficiency, 2010, 17(6):51-53,114.
|
[2] |
王鑫. 基于检波器三维矢量组合的抽油机噪声压制方法研究[J]. 石油物探, 2011, 50(3):295-300,306,7.
|
[2] |
Wang X. Suppressing the noise from pumping unit based on 3-D vector combination of geophones[J]. Geophysical Prospecting for Petroleum, 2011, 50(3):295-300,306,7.
|
[3] |
赵彦青, 萧蕴诗. 基于盲源分离的抽油机噪声压制方法[J]. 计算机应用, 2014, 34(S1):349-351.
|
[3] |
Zhao Y Q, Xiao Y S. Method for noise suppression of pumping units based on blind source separation[J]. Journal of Computer Applications, 2014, 34(S1):349-351.
|
[4] |
李学良, 王晓凯, 赵昊, 等. 地震勘探中基于形态成分分析的抽油机噪声衰减方法[J]. 地球物理学进展, 2017, 32(2):657-663.
|
[4] |
Li X L, Wang X K, Zhao H, et al. Well-pump noise attenuating method based on morphological component analysis[J]. Progress in Geophysics, 2017, 32(2):657-663.
|
[5] |
Liu D W, Wang W, Wang X K, et al. Poststack seismic data denoising based on 3-D convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):1598-1629.
|
[6] |
Yuan Y J, Si X, Zheng Y. Ground-roll attenuation using generative adversarial networks[J]. Geophysics, 2020, 85(4):WA255-WA267.
|
[7] |
Kaur H, Fomel S, Pham N. Seismic ground-roll noise attenuation using deep learning[J]. Geophysical Prospecting, 2020, 68(7):2064-2077.
|
[8] |
张敏, 许一卓, 易继东. 基于可伸缩型注意力机制的神经网络地震数据去噪方法[J]. 物探与化探, 2024, 48(4):1065-1075.
|
[8] |
Zhang M, Xu Y Z, Yi J D. A method for seismic data denoising based on the neural network with a retractable attention mechanism[J]. Geophysical and Geochemical Exploration, 2024, 48(4):1065-1075.
|
[9] |
周慧, 孙成禹, 刘英昌, 等. 基于DC-UNet卷积神经网络的强噪声压制方法[J]. 物探与化探, 2023, 47(5):1288-1297.
|
[9] |
Zhou H, Sun C Y, Liu Y C, et al. A method for strong noise suppression based on DC-UNet[J]. Geophysical and Geochemical Exploration, 2023, 47(5):1288-1297.
|
[10] |
Sun J, Slang S, Elboth T, et al. A convolutional neural network approach to deblending seismic data[J]. Geophysics, 2020, 85(4):WA13-WA26.
|
[11] |
Zhao Y X, Li Y, Dong X T, et al. Low-frequency noise suppression method based on improved DnCNN in desert seismic data[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(5):811-815.
|
[12] |
Yu S, Ma J, Wang W. Deep learning for denoising[J]. Geophysics, 2019, 84(6):V333-V350.
|
[13] |
Ren H P, Li C, Wen X T, et al. Suppressing well-pump noise from seismic data based on multilayer generator network[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:8028705.
|
[14] |
Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.
|
[15] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014.
|
|
|
|