Ensemble‐based methods are among the state‐of‐the‐art history‐matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history‐matching performance. It is customary to equip an ensemble history‐matching algorithm with a localization scheme to prevent ensemble collapse. Conventional localization methods use distances between the physical locations of model variables and observations to modify the degree of the observations’ influence on model updates. Distance‐based localization methods work well in many problems, but they also suffer from dependence on the physical locations of both model variables and observations, the challenges in dealing with nonlocal and time‐lapse measurements, and the nonadaptivity to handling different types of model variables.
To enhance the applicability of localization to various history‐matching problems, we adopt an adaptive localization scheme that exploits the correlations between model variables and simulated observations. We elaborate how correlation‐based adaptive localization can overcome or mitigate issues arising in conventional distance‐based localization.
To demonstrate the efficacy of correlation‐based adaptive localization, we adopt an iterative ensemble smoother (iES) with the proposed localization scheme to history match the real production data of the Norne Field model, and we compare the history‐matching results with those obtained by using the iES with distance‐based localization. Our study indicates that when compared with distance‐based localization, correlation‐based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to use in practical history‐matching problems. As a result, the proposed correlation‐based localization scheme might serve as a viable alternative to conventional distance‐based localization.