TY - JOUR
T1 - Analysis of localization methods in Ensemble Kalman Filter with SVD analytical solution
AU - Jiang, Junzhe
AU - Gorell, Sheldon B.
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/4
Y1 - 2019/4
N2 - When localization methods used in reservoir history matching were introduced from outside the oil and gas industry, the differences between data assimilation for other industries and reservoir history matching were not considered. However, these differences can be extremely important when considering the validity of these methods. This problem is discussed within the context of the roles of covariance localization in detail in this paper. This paper starts with discussion of relations between two significant problems, ill-posedness and spurious correlations, and their solution methods. This analysis leads to a conclusion about two appropriate roles of the covariance localization method. This conclusion is used to validate the cross-covariance localization methods used in reservoir simulation. In addition, for the cross-covariance localization in reservoir history matching, there are many different processes, especially different correlation functions, proposed by different authors. This makes it difficult to choose a proper method when solving history matching or data assimilation problems. The performance of different cross-covariance localization correlation functions is also analyzed theoretically and experimentally in this paper.
AB - When localization methods used in reservoir history matching were introduced from outside the oil and gas industry, the differences between data assimilation for other industries and reservoir history matching were not considered. However, these differences can be extremely important when considering the validity of these methods. This problem is discussed within the context of the roles of covariance localization in detail in this paper. This paper starts with discussion of relations between two significant problems, ill-posedness and spurious correlations, and their solution methods. This analysis leads to a conclusion about two appropriate roles of the covariance localization method. This conclusion is used to validate the cross-covariance localization methods used in reservoir simulation. In addition, for the cross-covariance localization in reservoir history matching, there are many different processes, especially different correlation functions, proposed by different authors. This makes it difficult to choose a proper method when solving history matching or data assimilation problems. The performance of different cross-covariance localization correlation functions is also analyzed theoretically and experimentally in this paper.
KW - Ensemble Kalman Filter
KW - History Matching
KW - Localization
KW - Parameterization
KW - Reservoir Simulation
UR - http://www.scopus.com/inward/record.url?scp=85059892133&partnerID=8YFLogxK
U2 - 10.1016/j.petrol.2019.01.030
DO - 10.1016/j.petrol.2019.01.030
M3 - Article
AN - SCOPUS:85059892133
SN - 0920-4105
VL - 175
SP - 919
EP - 931
JO - Journal of Petroleum Science and Engineering
JF - Journal of Petroleum Science and Engineering
ER -