@article{b7931ca9101446769d54df2fd821c569,
title = "Verification of a real-time ensemble-based method for updating earth model based on GAN",
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.",
keywords = "Deep neural network, Ensemble randomized maximum likelihood, Generative Adversarial Network, Geosteering, Machine learning",
author = "Kristian Fossum and Sergey Alyaev and Jan Tveranger and Elsheikh, {Ahmed H.}",
note = "Funding Information: Part of the work was performed within the project {\textquoteleft}Geosteering for IOR{\textquoteright} (NFR-Petromaks2 project no. 268122) which is funded by the Research Council of Norway , Aker BP ASA , Equinor Energy AS , V{\aa}r Energi ASA and Baker Hughes Norge AS . We would like to thank Emerson Roxar for providing an academic licence for RMS 11.1. used for the geo-modeling in this study. Funding Information: This work is part of the Center for Research-based Innovation DigiWells: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint (NFR SFI project no. 309589, https://DigiWells.no ). The center is a cooperation of NORCE Norwegian Research Centre, the University of Stavanger, the Norwegian University of Science and Technology (NTNU), and the University of Bergen. It is funded by Aker BP ASA , ConocoPhillips Skandinavia AS , Equinor Energy AS , Lundin Energy Norway AS , TotalEnergies EP Norge AS , V{\aa}r Energi ASA , Wintershall Dea Norge AS , and the Research Council of Norway . Funding Information: This work is part of the Center for Research-based Innovation DigiWells: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint (NFR SFI project no. 309589, https://DigiWells.no). The center is a cooperation of NORCE Norwegian Research Centre, the University of Stavanger, the Norwegian University of Science and Technology (NTNU), and the University of Bergen. It is funded by Aker BP ASA, ConocoPhillips Skandinavia AS, Equinor Energy AS, Lundin Energy Norway AS, TotalEnergies EP Norge AS, V{\aa}r Energi ASA, Wintershall Dea Norge AS, and the Research Council of Norway. Part of the work was performed within the project {\textquoteleft}Geosteering for IOR{\textquoteright} (NFR-Petromaks2 project no. 268122) which is funded by the Research Council of Norway, Aker BP ASA, Equinor Energy AS, V{\aa}r Energi ASA and Baker Hughes Norge AS. We would like to thank Emerson Roxar for providing an academic licence for RMS 11.1. used for the geo-modeling in this study. Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = nov,
doi = "10.1016/j.jocs.2022.101876",
language = "English",
volume = "65",
journal = "Journal of Computational Science",
issn = "1877-7503",
publisher = "Elsevier",
}