Blar i NORCE vitenarkiv på forfatter "Oliver, Dean"

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    • 4D seismic history matching 

      Oliver, Dean; Fossum, Kristian; Bhakta, Tuhin; Sandø, Ivar; Nævdal, Geir; Lorentzen, Rolf Johan (Journal article; Peer reviewed, 2021)
      Reservoir simulation models are used to forecast future reservoir behavior and to optimally manage reservoir production. These models require specification of hundreds of thousands of parameters, some of which may be ...

    • Fast robust optimization using bias correction applied to the mean model 

      Wang, Lingya; Oliver, Dean (Peer reviewed; Journal article, 2020)
      Ensemble methods are remarkably powerful for quantifying geological uncertainty. However, the use of the ensemble of reservoir models for robust optimization (RO) can be computationally demanding. The straightforward ...

    • Hybrid Iterative Ensemble Smoother for History Matching of Hierarchical Models 

      Oliver, Dean (Peer reviewed; Journal article, 2022)
      The choice of a prior model can have a large impact on the ability to assimilate data. In standard applications of ensemble-based data assimilation, all realizations in the initial ensemble are generated from the same ...

    • Improving Sequential Decisions – Efficiently Accounting for Future Learning 

      Wang, Lingya; Oliver, Dean (Peer reviewed; Journal article, 2021)
      In sequential field development planning, past decisions not only directly affect the maximum achievable expected NPV but also influence the future information that can be used to reduce geological uncertainty. To act ...

    • Quantifying prior model complexity for subsurface reservoir models 

      Mioratina, Nomenjanahary Tanteliniaina; Oliver, Dean (Peer reviewed; Journal article, 2023)
      In Bayesian approaches to history matching for subsurface inference, the prior model specifies the uncertain model parameters and the joint probability of those parameters before incorporating production-related data. A ...

    • Randomized maximum likelihood based posterior sampling 

      Ba, Yuming; de Wiljes, Jana; Oliver, Dean; Reich, Sebastian (Peer reviewed; Journal article, 2021)
      Minimization of a stochastic cost function is commonly used for approximate sampling in high-dimensional Bayesian inverse problems with Gaussian prior distributions and multimodal posterior distributions. The density of ...