Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling
Zhao Jian 1,2, Lu Jun-Qiang♦1,2, Wu Jin-Ping 3, Men Bai-Yong 1,2, and Chen Hong-Zhi 1,2
1. State Key Laboratory of Petroleum Resources and Prospecting at China University of Petroleum, Beijing 102249, China.
2. College of Geophysics, China University of Petroleum, Beijing 102249, China.
3. SINOPEC Engineering Technology Research Institute, Beijing 102200, China.
Abstract：
The existing methods for extracting the arrival time and amplitude of ultrasonic echo cannot effectively avoid the local interference of ultrasonic signals while drilling, which leads to poor accuracy of the echo arrival time and amplitude extracted by an ultrasonic imaging logging-while-drilling tool. In this study, a demodulation algorithm is used to preprocess the ultrasonic simulation signals while drilling, and we design a backpropagation neural network model to fit the relationship between the waveform data and time and amplitude. An ultrasonic imaging logging model is established, and the finite element simulation software is used for forward modeling. The response under different measurement conditions is simulated by changing the model parameters, which are used as the input layer of the neural network model; The ultrasonic echo signal is considered as a low-frequency signal modulated by a high-frequency carrier signal, and a low-pass filter is designed to remove the high-frequency signal and obtain the low-frequency envelope signal. Then the amplitude of the envelope signal and its corresponding time are extracted as an output layer of the neural network model. By comparing the application effects of the various training methods, we find that the conjugate gradient descent method is the most suitable method for solving the neural network model. The performance of the neural network model is tested using 11 groups of simulation test data, which verify the effectiveness of the model and lay the foundation for further practical application.
2021, Vol. 18 
