This work provides an advanced prototype for pipeline and wellbore tube inspection using electromagnetic (EM) resonance coupling, electromagnetic (EM) coupling, and machine learning. Utilizing only two transmitters and eight sensor coils, the described device can detect and characterize inner, outer, and total metal loss in a pipe's body. A defect in the pipe body alters the impedance of the transmitter and receiver coils and the mutual coupling between them, resulting in a relative change in one or more Rx outputs. A framework for artificial neural networks (ANN) is designed to assess the eight outputs and generate a two-dimensional map of the pipe cross-section. The ANN is trained using a finite-difference time-domain electromagnetic forward solver. The designed prototype is tested and validated through simulations and an experimental setup. The results demonstrate that the tool, with the assistance of the ANN, could not only detect single and double flaws with either complete or partial metal loss, but also define the defect's size, location, and depth.
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.
Pipe strings are commonly employed in the oil and gas sector, where they are subjected to immense strains and highly corrosive fluids. Electromagnetic (EM) based inspection tools are widely used and typically require one or more excitation sources (i.e., transmitter coil(s)), as well as sensing devices, which adds complexity and expense. By utilizing the inductive sensing principle, this work introduces a novel inspection method with no transmitter coil(s) capable of characterizing inner and full metal loss on the pipe's body. The proposed technique has been modeled and simulated using the commercial EM solver ANSYS Maxwell, as well as a proof of concept prototype, has been built. The results reveal that the tool could detect full and partial metal loss and fully characterize the defect's size, location, and depth.
The structural stability of wellbores depends on the concentric steel casings that are lowered into the wells and cemented in place. Such casings are often subjected to intense forces and high pressure, as well as being exposed to corrosive elements. As a result, defects such as pits, cracks, and other forms of metal loss inevitably occur on the casings. The presence of defects poses a threat to wellbore integrity that increases overtime as the metal losses increase in both depth of penetration and surface area, which may result in severe environmental and financial damage if left unchecked. Hence, many acoustic, visual, and electromagnetic (EM) inspection methods have been developed to assess the health of casings to facilitate risk management decisions. EM inspection methods are widely used because of their ability to detect metal loss on multiple concentric casings while being largely unaffected by the cement between the casings. While visual and acoustic methods generally produce results that are readily interpretable, EM measurements are often more difficult to utilize due to their high nonlinearity. This research investigates the EM inspection of wellbore casings using the near- and remote-field eddy current (NFEC and RFEC) methods. Cross-sectional images are reconstructed by a hybrid neural network (HNN) with two parallel modules that map EM measurements to the pixels of the images. A specialized neural network module is designed for each of these methods. Both modules include convolutional and recurrent layers in their structures to extract spatial and sequential attributes from EM data. Using this approach, the physical locations of metal loss and casing material are inherently represented by the coordinates of the pixels on the reconstructed image, while the values of the pixels represent the probability of metal loss at their location. In addition, in-depth analyses show that this approach is generalizable to metal loss scenarios that are different in terms of shape and location from the training data.
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