Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 21, 6069-6089, 2017
https://doi.org/10.5194/hess-21-6069-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
01 Dec 2017
Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies
Anne-Sophie Høyer1, Giulio Vignoli1,2, Thomas Mejer Hansen3, Le Thanh Vu4, Donald A. Keefer5, and Flemming Jørgensen1 1Groundwater and Quaternary Geology Mapping Department, GEUS, Aarhus, 8000, Denmark
2Department of Civil, Environmental Engineering and Architecture (DICAAR), University of Cagliari, Cagliari, 09123, Italy
3Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100, Denmark
4I-GIS, Risskov, 8240, Denmark
5Illinois State Geological Survey, Prairie Research Institute, University of Illinois, Champaign, Illinois 61820, USA
Abstract. 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 2810 km2 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 100 m  ×  100 m  ×  5 m. 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 90 km2. 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.

Citation: Høyer, A.-S., Vignoli, G., Hansen, T. M., Vu, L. T., Keefer, D. A., and Jørgensen, F.: Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies, Hydrol. Earth Syst. Sci., 21, 6069-6089, https://doi.org/10.5194/hess-21-6069-2017, 2017.
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We present a novel approach for 3-D geostatistical simulations. It includes practical strategies for the development of realistic 3-D training images and for incorporating the diverse geological and geophysical inputs together with their uncertainty levels (due to measurement inaccuracies and scale mismatch). Inputs consist of well logs, seismics, and an existing 3-D geomodel. The simulation domain (45 million voxels) coincides with the Miocene unit over 2810 km2 across the Danish–German border.
We present a novel approach for 3-D geostatistical simulations. It includes practical strategies...
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