Blar i NORCE vitenarkiv på forfatter "Evensen, Geir"

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    • 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method 

      Soares, Ricardo; Luo, Xiaodong; Evensen, Geir; Bhakta, Tuhin (Peer reviewed; Journal article, 2020)
      It is possible to improve oil-reservoir simulation models by conditioning them on 4D seismic data. Computational issues may arise related to both storage and CPU time due to the size of the 4D seismic dataset. An approach ...

    • An ensemble-based decision workflow for reservoir management 

      Chang, Yuqing; Evensen, Geir (Peer reviewed; Journal article, 2022)
      It is challenging to make optimal field development and reservoir management decisions with diminishing resources and low-emission requirements. For an optimal exploitation of the reservoir fluids, it is necessary to ...

    • Formulating the history matching problem with consistent error statistics 

      Evensen, Geir (Peer reviewed; Journal article, 2021)
      It is common to formulate the history-matching problem using Bayes’ theorem. From Bayes’, the conditional probability density function (pdf) of the uncertain model parameters is proportional to the prior pdf of the model ...

    • Learning from weather and climate science to prepare for a future pandemic 

      Schemm, Sebastian; Grund, Dana; Knutti, Reto; Wernli, Heini; Ackermann, Martin; Evensen, Geir (Peer reviewed; Journal article, 2023)
      Established pandemic models have yielded mixed results to track and forecast the SARS-CoV-2 pandemic. To prepare for future outbreaks, the disease-modeling community can improve their modeling capabilities by learning from ...

    • p-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching 

      Stordal, Andreas Størksen; Moraes, Rafael J.; Raanes, Patrick N.; Evensen, Geir (Journal article; Peer reviewed, 2021)
      A Bayesian method of inference known as “Stein variational gradient descent” was recently implemented for data assimilation problems, under the heading of “mapping particle filter”. In this manuscript, the algorithm is ...

    • A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering 

      Popov, Andrey A.; Sandu, Adrian; Nino-Ruiz, Elias D.; Evensen, Geir (Peer reviewed; Journal article, 2023)
      The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than ...