Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

A.-S. Hoyer, G. Vignoli, T.M. Hansen, L.T. Vu, D.A. Keefer, F. Jorgensen

Research output: Working paperPreprint


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. [Hydrol.Earth Syst.Sci. 21 (2017) doi:10.5194/hess-21-6069-2017].
Original languageEnglish (US)
StateIn preparation - 2017

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