Acoustic detection of gas leakage under tubing liquid level based on convolutional neural network in gas wells
Tubing leakage in gas wells has always been a common problem in oil and gas production.Aiming at the problem that the acoustic characteristics of tubing leakage under liquid level are unknown and difficult to identify,a gas well tubing leakage detection method under liquid level based on acoustic wave and convolutional neural network(CNN)is established.Firstly,a visual tubing leakage simulation experiment device was built,and the leakage acoustic wave detection experiment under typical working conditions was carried out.Then,the leakage acoustic wave signal under the liquid level was analyzed in the time domain and frequency domain.Finally,the self-built CNN model was used for tubing leakage detection.The time-frequency diagram obtained by the short-time Fourier transform of the acoustic wave signal was used as the input of the model for model parameter training.The results show that the root mean square value and absolute mean value of leakage acoustic wave under liquid level increase with the increase of leakage flow rate and liquid level depth.The time-frequency spectrum of leakage acoustic wave under liquid level is obviously different from other working conditions.The accuracy of leakage identification of the proposed model can reach 99.33％.Compared with the recognition model based on extreme learning machine and support vector machine,the proposed model has higher recognition accuracy,which verifies the effectiveness of the proposed method.
Gas wellTubing leakage under liquid levelConvolutional neural networkLeakage identificationAcoustic wave
NETL
NSTL
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