Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-567
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
25 Nov 2016
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies
Anne-Sophie Høyer1, Giulio Vignoli1, Thomas Mejer Hansen2, Le Thanh Vu3, Donald A. Keefer4, and Flemming Jørgensen1 1Groundwater and Quaternary Geology Mapping Department, GEUS, Aarhus, 8000, Denmark
2Niels-Bohr Institute, University of Copenhagen, Copenhagen, 2100, Denmark
3I-GIS, Risskov, 8240, Denmark
4Illinois State Geological Survey, Champaign, IL 61820, USA
Abstract. Most studies about 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 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D 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 3D 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 sand/clay spatial trends. The training image is constructed as a small 3D 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, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible 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., Mejer Hansen, T., Vu, L. T., Keefer, D. A., and Jørgensen, F.: Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-567, in review, 2016.
Anne-Sophie Høyer et al.
Anne-Sophie Høyer et al.
Anne-Sophie Høyer et al.

Viewed

Total article views: 202 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
142 56 4 202 2 5

Views and downloads (calculated since 25 Nov 2016)

Cumulative views and downloads (calculated since 25 Nov 2016)

Viewed (geographical distribution)

Total article views: 202 (including HTML, PDF, and XML)

Thereof 201 with geography defined and 1 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 24 Mar 2017
Publications Copernicus
Download
Short summary
We present a novel approach for 3D geostatistical simulations. It includes practical strategies for the development of realistic 3D 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 3D 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 3D geostatistical simulations. It includes practical strategies...
Share