DOI: 10.5194/hess-21-6069-2017
Scopus记录号: 2-s2.0-85037030735
论文题名: Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies
作者: Høyer A ; -S ; , Vignoli G ; , Hansen T ; M ; , Vu L ; T ; , Keefer D ; A ; , Jørgensen F
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 12 起始页码: 6069
结束页码: 6089
语种: 英语
Scopus关键词: Geology
; Iterative methods
; Seismology
; Uncertainty analysis
; Water wells
; Well logging
; Geological characteristics
; Geological environment
; Geostatistical modelling
; Geostatistical simulation
; High resolution seismic data
; Hydrogeological models
; Multiple-point statistics
; Structural uncertainty
; Planning
; data quality
; data set
; geophysics
; geostatistics
; hydrogeology
; image analysis
; simulation
; statistical analysis
; three-dimensional modeling
; uncertainty analysis
; Denmark
英文摘要: Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100m × 100m × 5m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78977
Appears in Collections: 气候变化事实与影响
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作者单位: Groundwater and Quaternary Geology Mapping Department, GEUS, Aarhus, Denmark; Department of Civil, Environmental Engineering and Architecture (DICAAR), University of Cagliari, Cagliari, Italy; Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark; I-GIS, Risskov, Denmark; Illinois State Geological Survey, Prairie Research Institute, University of Illinois, Champaign, IL, United States
Recommended Citation:
Høyer A,-S,, Vignoli G,et al. Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies[J]. Hydrology and Earth System Sciences,2017-01-01,21(12)