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Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies | | Anne-Sophie Høyer
; Giulio Vignoli
; Thomas Mejer Hansen
; Le Thanh Vu
; Donald A. Keefer
; Flemming Jørgensen
; | | Date: |
21 Nov 2020 | | 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 on the estimation of facies-level structural
uncertainty. Less attention is paid to the input data and the construction of
Training Images (TIs). E.g. even though the TI should capture a set of spatial
geological characteristics, the majority of the research still relies on 2D or
quasi-3D training images. Here, we demonstrate a novel strategy for 3D MPS
modelling characterized by (i) realistic 3D TIs and (ii) an effective workflow
for incorporating a diverse group of geological and geophysical data sets. The
study covers 2810 km^2 in southern 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 x 10^6 voxels with size 100 m x 100 m x
5 m. Data used for the modelling include water well logs, 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 spatial trends. The TI is constructed as a
relatively small 3D voxel model covering an area of 90 km^2. We use an
iterative training image development strategy and find that even slight
modifications in the TI 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 TI and effectively
handle different types of input information to perform large-scale
geostatistical modelling | | Source: | arXiv, 2011.10745 | | Services: | Forum | Review | PDF | Favorites | |
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