Corrosion Monitoring With EM Induction Tool in 3 Outer Casing Strings through a 7 Inch Chrome Production Tubing Scenario

Castaneda, Claudio; Higuera, Josue; Portela, Fernando; Benavides, Marcia; Valstar, Dirk; Barnejee, Shuvo

doi:10.2118/168277-ms

Cited by 5 publications

(3 citation statements)

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“…where ∆𝑥, ∆𝑦, and ∆𝑧 are the dimensions of the 3D spatial grid cells. Generally, the signal frequencies used in EM casing inspection are low (Martin et al 2017, Castaneda et al 2014, which results in signal wavelengths that are much larger than the inspected targets. However, the simulation grid sizes must remain sufficiently small to ensure good resolutions with respect to the casings.…”

Section: Fdtd Forward Modelmentioning

confidence: 99%

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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Section: Fdtd Forward Modelmentioning

confidence: 99%

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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“…Electromagnetic wellbore integrity tools operate in the low-frequency range (Martin et al 2017b, Castaneda et al 2014, Brill et al 2011. In this case, Maxwell's equation-based forward solvers can be used to simulate possible defect scenarios.…”

Section: Forward Modelmentioning

confidence: 99%

EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML

Ooi

Ozakin

Mostafa

et al. 2021

SPE Offshore Europe Conference &Amp; Exhibition

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In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.

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“…Two frequencies are often used for calibration purposes. Some frequency-domain electromagnetic defectoscopes are designed to scan only the first barrier (Donald C. Vogtsberger, 2005), while others can scan several barriers at once but cannot tell which of them is corroded (Claudio Castaneda, 2014). The third barrier heavily distorts the overall picture and makes it difficult to assess metal loss and the total barrier thickness even roughly (Thilo Michael Brill, 2011).…”

Section: Principles Of Frequency-domain Corrosion Detectionmentioning

confidence: 99%

Triple-Barrier Thickness Scanning Using Through-Tubing Pulse-Magnetic Logging Tool

Ansari¹,

Libdi²,

Khan³

et al. 2015

All Days

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Servicing oil and gas wells requires their integrity assessment both during operation and before abandonment. One of the main objectives in well integrity analysis is the location of metal losses in tubing and/or casing caused by corrosion, erosion or other types of pipe damage. The first three metal barriers (normally tubing, production casing and intermediate casing) are of the most interest to the industry. Dual string completions is an additional complication to through-tubing assessment of the second and third barriers.Magnetic Imaging Defectoscope (MID) is an electromagnetic scanning tool that recor ds magnetisation decays induced by high-power electromagnetic pulses. Metal pipe barriers contribute to magnetisation decays at different times depending on their diameters, which makes it possible to differentiate each of them and determine their individual thicknesses. Thickness determination requires the numerical finite-element modelling of each recorded magnetisation decay and iterative fitting of the properties and thickness of every metal barrier to the actual tool readings. A data array can store hundreds of thousands decays, and the data processing optimisation loop therefore requires parallel computing with multi-core processors to process data within a reasonable time frame. The MID hardware and interpretation algorithm have been tested on multiple laboratory stands simulating various downhole multi-barrier completions from 2-7/8Љ up to 13 3/8Љ pipes with artificial defects of various shapes and sizes ranging from 7 mm to 140 mm. This paper presents laboratory results and three selected field cases demonstrating the application of Magnetic Imaging Defectoscopy (MID) in single-string and dual-string completions for thickness evaluation of three barriers independently by a memory through-tubing survey.• Well W-01 with metal losses found at the same depth in the second and third barriers, i.e. 9 5/8Љ and 13 3/8Љ casings • Well W-02 with metal loss found in the second barrier, i.e. 9 5/8Љ casing, in an interval containing two strings. This corrosion has been confirmed by a repeated MID survey after pulling out the completion • Well W-03 with through-hole metal loss found in the second and third barriers, i.e. large-diameter 9 5/8Љ and 13 3/8Љ casings, through a 7Љ liner. This through-hole corrosion has been confirmed independently by High-Precision Temperature Logging and Spectral Noise LoggingThe above metal losses have been located by through-tubing memory surveys in offshore wells that were to be abandoned. The results of the MID surveys were then used to design environmentally safe abandonment procedures.

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Corrosion Monitoring With EM Induction Tool in 3 Outer Casing Strings through a 7 Inch Chrome Production Tubing Scenario

Cited by 5 publications

References 0 publications

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

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

EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML

Triple-Barrier Thickness Scanning Using Through-Tubing Pulse-Magnetic Logging Tool

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