STUDY ON TIME-OF-FLIGHT ESTIMATION IN ULTRASONIC WELL LOGGING TOOL: MODEL-DRIVEN TRANSFER LEARNING

Time-of-flight (ToF) of ultrasonic waves is essential for petroleum well logging to draw borehole-wall images. This paper proposed a method that boosted accuracy of ToFs estimation in a complex geological environment. Unlike other classical methods, the proposed one adopts a one-dimensional convolutional neural network (1D-CNN) as a backbone to extract latent information related to ToF. For handling the shortage of ultrasonic waves with annotated ToFs, theoretical ultrasonic waves generated by manifold mathematical models are utilized as source domain to train the model, which is applied to estimate practical ultrasonic ToFs. Furthermore, since the distribution divergences exist between theoretical ultrasonic models and practical ones, Maximum Mean Discrepancy (MMD) and CORrelation ALignment (CORAL) as discrepancy loss functions are used to evaluate the distribution divergences between two domain datasets and to optimize the entire model. Tests on the ultrasonic waves acquired by an experimental well logging device demonstrate the proposed method has satisfactory performances.