dc.contributor.author | Fossum, Kristian | |
dc.contributor.author | Alyaev, Sergey | |
dc.contributor.author | Tveranger, Jan | |
dc.contributor.author | Elsheikh, Ahmed H. | |
dc.date.accessioned | 2023-03-30T12:35:44Z | |
dc.date.available | 2023-03-30T12:35:44Z | |
dc.date.created | 2022-11-03T14:40:32Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Journal of Computational Science. 2022, 65 . | en_US |
dc.identifier.issn | 1877-7503 | |
dc.identifier.uri | https://hdl.handle.net/11250/3061192 | |
dc.description.abstract | The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ensemble Randomized Maximum Likelihood (EnRML) for rapid updating of subsurface uncertainty. This real-time ensemble method is known to be approximate for non-linear forward models and might therefore produce inaccurate and/or biased posterior solutions when combined with a highly non-linear model arising from the neural-network modeling sequences. This paper illustrates the predictive ability of EnRML on several examples where we assimilate local extra-deep electromagnetic logs. Statistical verification with MCMC confirms that the proposed workflow can produce reliable results required for geosteering wells. | en_US |
dc.language.iso | eng | en_US |
dc.relation.uri | https://reader.elsevier.com/reader/sd/pii/S1877750322002356?token=612ADBB0BCF96E91D3B41E66E2F9229EA44FB93ABAA952E6B7F19D911706B9B27887F4BCB8E9F8CE9061C8E29EBF4408&originRegion=eu-west-1&originCreation | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Verification of a real-time ensemble-based method for updating earth model based on GAN | en_US |
dc.title.alternative | Verification of a real-time ensemble-based method for updating earth model based on GAN | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.rights.holder | © 2022 The Author(s) | en_US |
dc.description.version | publishedVersion | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.1016/j.jocs.2022.101876 | |
dc.identifier.cristin | 2068715 | |
dc.source.journal | Journal of Computational Science | en_US |
dc.source.volume | 65 | en_US |
dc.source.pagenumber | 11 | en_US |
dc.relation.project | Norges forskningsråd: 309589 | en_US |