Ensemble-based methods are among the state-of-the-art history matching algorithms. In practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching algorithm with a certain localization scheme. Conventional localization methods use distances between physical locations of model variables and observations to modify the degree of observations' influence on model updates. Distance- based localization methods work well in many problems, but they also suffer from some long-standing issues, including, for instance, the dependence on the presence of physical locations of both model variables and observations, the challenges in dealing with nonlocal and time-lapse observations, and the non-adaptivity to handle different types of model variables. To enhance the applicability of localization to various history matching problems, we propose to adopt an adaptive localization scheme that exploits the correlations between model variables and observations for localization. We elaborate how correlation-based adaptive localization can mitigate or overcome the noticed issues arising in conventional distance-based localization.

To demonstrate the efficacy of correlation-based adaptive localization, we apply it to history-match the real production data of the full Norne field model using an iterative ensemble smoother (iES), and compare the history matching results to those obtained by using the same iES but with distance-based localization. Our study indicates that, in comparison to distance-based localization, correlation- based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to implement and use in practical history matching problems. As a result, the proposed correlation-based localization scheme may serve as a viable alternative to conventional distance-based localization.

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