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dc.contributor.authorOoi, Guang An
dc.contributor.authorÖzakin, Mehmet Burak
dc.contributor.authorMostafa, Tarek Mahmoud Atia
dc.contributor.authorBagci, Hakan
dc.contributor.authorAhmed, Shehab
dc.contributor.authorLarbi Zeghlache, Mohamed
dc.date.accessioned2021-09-13T06:52:53Z
dc.date.available2021-09-13T06:52:53Z
dc.date.issued2021-09-07
dc.identifier.citationOoi, G. A., Özakin, M. B., Mostafa, T. M., Bagci, H., Ahmed, S., & Larbi Zeghlache, M. (2021). EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML. Day 3 Thu, September 09, 2021. doi:10.2118/205404-ms
dc.identifier.doi10.2118/205404-ms
dc.identifier.urihttp://hdl.handle.net/10754/671182
dc.description.abstractIn 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.
dc.publisherSPE
dc.relation.urlhttps://onepetro.org/SPEOE/proceedings/21OE/3-21OE/D032S017R002/469045
dc.rightsArchived with thanks to SPE
dc.titleEM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML
dc.typeConference Paper
dc.contributor.departmentAli I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
dc.contributor.departmentComputational Electromagnetics Laboratory
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentElectrical and Computer Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.conference.date7 - 10 September 2021
dc.conference.nameSPE Offshore Europe Conference & Exhibition
dc.conference.locationvirtually
dc.eprint.versionPost-print
dc.contributor.institutionSaudi Aramco
kaust.personOoi, Guang An
kaust.personÖzakin, Mehmet Burak
kaust.personMostafa, Tarek Mahmoud Atia
kaust.personBagci, Hakan
kaust.personAhmed, Shehab
refterms.dateFOA2021-09-19T07:34:44Z
dc.date.published-online2021-09-07
dc.date.published-print2021-09-07


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