TY - GEN
T1 - Multi-Frequency Data Acquisition Model and Hybrid Neural Network for Precise Electromagnetic Wellbore Casing Inspection
AU - Ooi, Guang An
AU - Khater, Moutazbellah
AU - Ozakin, Mehmet Burak
AU - Mostafa, Tarek M.
AU - Bagci, Hakan
AU - Ahmed, Shehab
N1 - Publisher Copyright:
Copyright © 2022, Society of Petroleum Engineers.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85143066096&partnerID=8YFLogxK
U2 - 10.2118/211807-MS
DO - 10.2118/211807-MS
M3 - Conference contribution
AN - SCOPUS:85143066096
T3 - Society of Petroleum Engineers - ADIPEC 2022
BT - Society of Petroleum Engineers - ADIPEC 2022
PB - Society of Petroleum Engineers
T2 - Abu Dhabi International Petroleum Exhibition and Conference 2022, ADIPEC 2022
Y2 - 31 October 2022 through 3 November 2022
ER -