dc.contributor.author | Alyaev, Sergey | |
dc.contributor.author | Shahriari, Mostafa | |
dc.contributor.author | Pardo, David | |
dc.contributor.author | Omella, Ángel Javier | |
dc.contributor.author | Larsen, David Selvåg | |
dc.contributor.author | Jahani, Nazanin | |
dc.contributor.author | Suter, Erich Christian | |
dc.date.accessioned | 2024-07-03T08:59:13Z | |
dc.date.available | 2024-07-03T08:59:13Z | |
dc.date.created | 2021-06-04T22:01:02Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Geophysics. 2021, 86 (3), E269-E281. | en_US |
dc.identifier.issn | 0016-8033 | |
dc.identifier.uri | https://hdl.handle.net/11250/3137644 | |
dc.description.abstract | Modern 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.abstract | Modeling extra-deep electromagnetic logs using a deep neural network | en_US |
dc.language.iso | eng | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Modeling extra-deep electromagnetic logs using a deep neural network | en_US |
dc.title.alternative | Modeling extra-deep electromagnetic logs using a deep neural network | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.rights.holder | © 2021 The Authors | en_US |
dc.description.version | publishedVersion | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.doi | 10.1190/geo2020-0389.1 | |
dc.identifier.cristin | 1913853 | |
dc.source.journal | Geophysics | en_US |
dc.source.volume | 86 | en_US |
dc.source.issue | 3 | en_US |
dc.source.pagenumber | E269-E281 | en_US |
dc.relation.project | Norges forskningsråd: 268122 | en_US |