Wireline Spooling Automation through Computer Vision

In the oil and gas industry, Wireline is used to lower logging toolstrings into a well for well intervention and reservoir evaluation. While the toolstring is retrieved from the well, the Wireline cable, typically under tension, is spooled on a drum. The cable could be thousands of feet long and can stack on the drum for as many as ∼100 layers, with each layer consisting of ∼100 wraps. Proper spooling is important because failure could lead to severe cable damage. Two computer-vision-based applications have been developed: the first one uses a convolutional neural network (CNN) and an encode-decoder network to detect spooling anomaly, the second one uses a similar approach to estimate the cable position in real time.

We evaluated 13 base networks for their convergence rate, accuracy and F1 score. We found that for the two different applications, the winner base network is different. For anomaly detection, the Inception-V3 base network performs the best, while for cable position prediction, the VGG-19 network outperforms others. We optimized the networks using TensorRT. To remove prediction flickering, we tested different filters and found an LSTM-based encoder-decoder network performs the best.