Correlation-Based Adaptive Localization for Ensemble-Based History Matching: Applied To the Norne Field Case Study

Abstract

Summary 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.

Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning

“…In our SHM framework, however, the effective observations (e.g., wavelet coefficients) resulting from sparse data representation do not have associated physical locations, and this makes it challenging to apply distance-based localization. As a result, we adopt a correlation-based adaptive localization scheme instead, which helps overcome the aforementioned problem, while achieving a few additional benefits (Luo et al, 2018b(Luo et al, , 2019. More details of the implementation of correlation-based adaptive localization will also be provided in the case-study part later.…”

“…In this work, the TSVD is conducted following the previous work Luo et al (2019). On the other hand, we adopt correlation-based adaptive localization, since in practice it tends to be more flexible than distance-based localization, as elaborated in Luo et al (2019).…”

“…In this work, the TSVD is conducted following the previous work Luo et al (2019). On the other hand, we adopt correlation-based adaptive localization, since in practice it tends to be more flexible than distance-based localization, as elaborated in Luo et al (2019). For more information of how correlation-based adaptive localization is conducted, readers are referred to Luo et al (2018bLuo et al ( , 2019; Lorentzen et al (2019).…”

Data assimilation with multiple types of observation boreholes via the ensemble Kalman filter embedded within stochastic moment equations

“…Iterative forms of the EnKF and ES, usually denoted by IEnKF (Gu and Oliver, 2007;Sakov et al, 2012) and IES (Chen and Oliver, 2013;Emerick and Reynolds, 2013;Luo et al, 2015;Chang et al, 2017;Li et al, 2018), have been developed to improve assimilation performance in scenarios characterized by strongly nonlinear behaviors. A variety of studies investigate challenges linked to such (ensemble) data assimilation algorithms, including, e.g., the possibility of coping with non-Gaussian model parameter distributions (Zhou et al, 2011;Li et al, 2018), physical unphysical results stemming from the estimation workflow (Wen and Chen, 2006;Song et al, 2014), or spurious correlations (Panzeri et al, 2013;Bauser et al, 2018;Luo et al, 2019;Soares et al, 2019). All of these works contribute to improve the robustness of these algorithms for parameter estimation in complex environmental systems.…”

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

“…There are also some schemes to update the model parameters selectively in EnKF. It is called as the localization scheme, and these schemes are divided into distance-based (Chen and Oliver 2010;Jung et al 2017b), attribute-based (e.g., streamline and temperature distribution) (Arroyo-Negrete et al 2008;Huang and Zeng 2016), and correlation-based process (Luo et al 2018(Luo et al , 2019.…”