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dc.contributor.authorAlyaev, S.
dc.contributor.authorShahriari, M.
dc.contributor.authorPardo, D. 
dc.contributor.authorOmella, A. J.
dc.contributor.authorLarsen, D.
dc.contributor.authorJahani, N.
dc.contributor.authorSuter, E.
dc.date.accessioned2022-01-03T10:16:07Z
dc.date.available2022-01-03T10:16:07Z
dc.date.issued2021-05
dc.identifier.issn0016-8033
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1409
dc.description.abstractModern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.en_US
dc.description.sponsorshipPOCTEFA 2014-2020 PIXIL (EFA362/19) MTM2016-76329-Ren_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleModeling extra-deep electromagnetic logs using a deep neural networken_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.arxiv2005.08919
dc.identifier.doi10.1190/geo2020-0389.1en_US
dc.relation.publisherversionhttps://library.seg.org/doi/10.1190/geo2020-0389.1en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGEOPHYSICSen_US
dc.volume.number86en_US
dc.issue.number3en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España