History matching of statistically anisotropic fields using the   Karhunen-Loeve expansion-based global parameterization technique

doi:10.1007/s10596-014-9409-z

CORC > 北京大学 > 工学院

	History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique
	Chang, Haibin ; Zhang, Dongxiao
刊名	computational geosciences
	2014
关键词	Reservoir history matching Statistically anisotropic field Global parameterization Karhunen-Loeve expansion ENSEMBLE KALMAN FILTER PROBABILITY PERTURBATION METHOD GRADUAL DEFORMATION ITERATIVE CALIBRATION FACIES DISTRIBUTION DATA ASSIMILATION POROUS-MEDIA EFFICIENT FLOW SIMULATIONS
DOI	10.1007/s10596-014-9409-z
英文摘要	Traditional ensemble-based history matching method, such as the ensemble Kalman filter and iterative ensemble filters, usually update reservoir parameter fields using numerical grid-based parameterization. Although a parameter constraint term in the objective function for deriving these methods exists, it is difficult to preserve the geological continuity of the parameter field in the updating process of these methods; this is especially the case in the estimation of statistically anisotropic fields (such as a statistically anisotropic Gaussian field and facies field with elongated facies) with uncertainties about the anisotropy direction. In this work, we propose a Karhunen-Loeve expansion-based global parameterization technique that is combined with the ensemble-based history matching method for inverse modeling of statistically anisotropic fields. By using the Karhunen-Loeve expansion, a Gaussian random field can be parameterized by a group of independent Gaussian random variables. For a facies field, we combine the Karhunen-Loeve expansion and the level set technique to perform the parameterization; that is, for each facies, we use a Gaussian random field and a level set algorithm to parameterize it, and the Gaussian random field is further parameterized by the Karhunen-Loeve expansion. We treat the independent Gaussian random variables in the Karhunen-Loeve expansion as the model parameters. When the anisotropy direction of the statistically anisotropic field is uncertain, we also treat it as a model parameter for updating. After model parameterization, we use the ensemble randomized maximum likelihood filter to perform history matching. Because of the nature of the Karhunen-Loeve expansion, the geostatistical characteristics of the parameter field can be preserved in the updating process. Synthetic cases are set up to test the performance of the proposed method. Numerical results show that the proposed method is suitable for estimating statistically anisotropic fields.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000336396700010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Interdisciplinary Applications; Geosciences, Multidisciplinary; SCI(E); 2; ARTICLE; haibinch@pku.edu.cn; 2; 265-282; 18
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/154295]
专题	工学院
推荐引用方式 GB/T 7714	Chang, Haibin,Zhang, Dongxiao. History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique[J]. computational geosciences,2014.
APA	Chang, Haibin,&Zhang, Dongxiao.(2014).History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique.computational geosciences.
MLA	Chang, Haibin,et al."History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique".computational geosciences (2014).