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dc.contributor.authorHosseini, Seyed Ehsan
dc.contributor.authorZonetti, Simone
dc.contributor.authorRomdhane, Anouar
dc.contributor.authorDupuy, Bastien
dc.contributor.authorArntsen, Børge
dc.description.abstractMonitoring of integrity of plugged and abandoned (P&A'ed) wells is of interest for the oil and gas industry and for CO2 storage. The purpose of this study is to develop artificial intelligence (AI)-based approaches to detect anomalies or defects when monitoring permanently plugged wells. The studied solution is based on the analysis of electromagnetic (EM) data. We consider an offshore setting where the EM signal is generated in presence of a P&A'ed well and the resulting electric field is recorded at the seafloor. Numerical simulations are used to train an AI algorithm to classify the modelled EM features into predefined well integrity classes. We consider four scenarios: (1) no well, (2) well with three 20 meters thick cement barriers of thickness, (3) well with three cement barriers of 60 meters thickness, and (4) well with three cement barriers of 100 meters thickness. Convolutional neural networks (CNNs) are tested as the AI algorithm in this study. After training the algorithm on 80% of the data, it shows an accuracy of 95.36% on the test data. P&A'ed well integrity monitoring currently remains limited to local observation and symptom identification, but this study shows that there is great potential for developing remote non-invasive well integrity monitoring techniques.en_US
dc.publisherSINTEF Academic Pressen_US
dc.relation.ispartofTCCS–11. CO2 Capture, Transport and Storage. Trondheim 22nd–23rd June 2021. Short Papers from the 11th International Trondheim CCS Conference
dc.relation.ispartofseriesSINTEF Proceedings;7
dc.rightsCC BY 4.0*
dc.subjectP&A'ed well integrityen_US
dc.subjectNon-invasive monitoringen_US
dc.subjectElectro-magnetic fieldsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectConvolutional Neural Networks (CNNs)en_US
dc.titleArtificial Intelligence for Well Integrity Monitoring Based on EM Dataen_US
dc.typePeer revieweden_US
dc.typeConference objecten_US
dc.rights.holder© 2021 The Authors. Published by SINTEF Academic Press.en_US
dc.subject.nsiVDP::Teknologi: 500en_US

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