Transient eddy current response to pulsed eddy current testing inside a ferromagnetic casing

Sun, Hongjun; Shi, Yibing; Zhang, Wei; Li, Yanjun

doi:10.1016/j.ndteint.2021.102587

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…ecently, with the increasing concern for safety in oil and gas production, the use of eddy current nondestructive testing (NDT) [1] for wellbore casings has undergone considerable investigation. As a general solution, pulsed (transient) eddy current (PEC) techniques [2,3] are employed for NDT of wellbore casings [4,5] as they enable rapid and accurate acquisition of low-frequency range data from time decay signals [6,7]. A borehole PEC system typically uses pulsed or transient signals as the coil excitation [8,9], and the metal pipe thickness as well as various defects can be determined by the amplitude and phase of the response received by a magnetic sensor, such as a coil sensor [10], hall sensor [11], and magnetic resistance sensor [12].…”

Section: Introductionmentioning

confidence: 99%

Space–Time Pulsed Eddy Current Array for NDT of Wellbore Casings Based on MIMO Technique

Liu,

Dang,

Yang

et al. 2024

IEEE Trans. Instrum. Meas.

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In this paper, we present a space-time pulsed eddy current (PEC) array for nondestructive testing (NDT) of wellbore casings using a multiple-input multiple-output (MIMO) technique. On the basis of a multilayered cylindrical wellbore model, a MIMO-PEC model is developed according to the phase shift characteristics of the PEC response corresponding to different transmitting-receiving (TR) distances. To achieve a more significant number of TR channels than in a traditional receiving array to reduce the complexity of the transmitting-receiving structure, a space-time transmission scheme is proposed to recover multiple TR channels. Moreover, a specific space-time MIMO array structure is designed to maximize the number of independent TR channels. The effectiveness of the proposed method is verified by applying the proposed MIMO array to a borehole PEC system for NDT of wellbore casings. The simulation and experimental results demonstrate that using the array structure and space-time transmission scheme proposed in this paper, a MIMO structure consisting of M transmitters and N receivers can be equivalent to a transceiver system with a single transmitter and M×N receivers. Thus, MIMO technique can be effectively applied to PEC-NDT systems and drastically reduce the cost and complexity of the PEC system.

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Section: Introductionmentioning

confidence: 99%

Space–Time Pulsed Eddy Current Array for NDT of Wellbore Casings Based on MIMO Technique

Liu,

Dang,

Yang

et al. 2024

IEEE Trans. Instrum. Meas.

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“…A variety of factors such as temperature drift, inspection tool movement speed, baseline wander (Dang et al 2020), and the non-uniformity in casing permeability contributes to the perturbations in EM data. In addition, other conductive wellbore components such as collars often greatly affect EM sensor readings (Sun et al 2022). Indeed, a significant amount of research has been focused on the signal processing and de-noising of EM measurements (Ling et al 2021, Liu et al 2014, Jun et al 2006, as well as the identification and removal of collar-induced anomalies (Sharar et al 2008, Wilson and Brooks 2001, Fouda et al 2020.…”

Section: Introductionmentioning

confidence: 99%

“…The complications in metal loss detection has led the development of inspection algorithms to estimate the average amount of remaining metal at a given layer of casings (Brill et al 2011), omitting directional and dimensional information. EM inspection methods such as pulsed eddy current (Sun et al 2022) and magnetic flux leakage (Assous et al 2021, Wilt et al 2020) are widely used approaches that make use of the variations in the amplitudes and phases of induced EM fields. To ensure robustness, such tools include multiple layers of sensors operating on one or more frequencies in order to enable the detection of metal loss on outer casings.…”

Section: Introductionmentioning

confidence: 99%

“…EM inspection measurements, acquired by arrays of sensors along casings and across specific timespans, can be regarded as such data. In both the near-and remote-field eddy current (NFEC and RFEC) (Sun et al 2022, Pasadas et al 2013 data acquisition methods in this study, multiple layers of measurements are acquired by circumferentially and axially distributed sensors as inspection tools scan along the interior of casings. This results in measurements with spatial, sequential, and temporal correlations (Mateu and Müller 2012), enhancing the accuracy and robustness of neural network predictions.…”

Section: Introductionmentioning

confidence: 99%

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Near-And Remote-Field Eddy Current Data Fusion: Wellbore Casing Inspection with Hybrid Neural Networks

Ooi

Mostafa

Khater

et al. 2022

Day 1 Mon, October 31, 2022

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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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“…Continuous and pulsed eddy current inspection tools (Aslanyan et al 2014, Sun et al 2022) commonly rely on measuring the secondary EM field generated by eddy currents induced on surrounding casings by transmitter (TX) coils. The continuous method measures the change in amplitude and phase of the EM fields, while pulsed methods measure the decay curve of the EM fields (Rourke et al 2014) in the period of time when the TX is switched off.…”

Section: Introductionmentioning

confidence: 99%

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

Ooi

Khater

Ozakin

et al. 2022

Day 2 Tue, November 01, 2022

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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.

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Transient eddy current response to pulsed eddy current testing inside a ferromagnetic casing

Cited by 13 publications

References 19 publications

Space–Time Pulsed Eddy Current Array for NDT of Wellbore Casings Based on MIMO Technique

Space–Time Pulsed Eddy Current Array for NDT of Wellbore Casings Based on MIMO Technique

Near-And Remote-Field Eddy Current Data Fusion: Wellbore Casing Inspection with Hybrid Neural Networks

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

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