Multi-Frequency Data Acquisition Model and Hybrid Neural Network for Precise Electromagnetic Wellbore Casing Inspection

Casing integrity inspection tools are indispensable in identifying defects that threaten the structural integrity of oil wells. In particular, electromagnetics-based (EM-based) inspection tools are commonly used for multi-casing corrosion imaging. These tools measure the scattered EM fields inside the inspected casings and generate estimations of metal loss properties. However, the interpretation of EM measurements is difficult due to their intrinsic nonlinearity with respect to defect characteristics. In this paper, a new machine learning-based inspection framework is developed to generate accurate cross-sectional images of casings to characterize metal loss location and shape. A hybrid neural network (HNN) consisting of a main structure that integrates both convolutional and recurrent layers, as well as a parallel cross-frequency module with convolutional filters predicts the cross-sectional images of the inspected casings. Metal losses on the inner surface of the inspected casing, as well as fully-penetrating losses, are detected using high-frequency signals. On the other hand, low-frequency signals enable the detection of metal losses on the outer surface, in addition to the two previous kinds of losses. The resulting inspection scheme requires only four receiver (RX) coils for each frequency of signals to accurately predict both the azimuthal location and size of defects.