LeadMoldNet: a convolutional neural network-based approach for lead impression analysis

In oilfield downhole overhaul operations, lead impression analysis is employed to detect casing damage and assess the internal condition of downhole equipment or pipelines. However, current lead impression analysis primarily relies on manual labor, resulting in inefficiencies and significant errors. This research suggests a deep learning-based approach for lead impression analysis in order to solve this problem, utilizing deep neural networks to predict lead impression types and thereby evaluate the internal state of downhole equipment or pipelines, ultimately aiding in the repair of downhole casings. Initially, a lead impression image dataset is constructed; subsequently, a lead impression recognition model, LeadMoldNet, based on convolutional neural networks, is designed. Finally, extensive experiments validate the effectiveness and performance of the LeadMoldNet model.