The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

Abstract

Abstract. Multiple-point geostatistical simulation (MPS)has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

Three-dimensional geological modelling of AEM resistivity data — A comparison of three methods

“…Lee et al, 2007). Geophysical data such as ground penetrating radar and seismic data have often been utilised in stochastic simulations (De Benedetto et al, 2012;Engdahl et al, 2010), but to our knowledge, AEM data have only been used in a few studies (Gunnink and Siemon, 2014;He et al, 2014aHe et al, , 2014b.…”

Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections

“…For a practical 3-D application, however, these attributes are notoriously difficult to characterize and model since the informed data we can acquire are very sparse. Two-point geostatistics (Pyrcz and Deutsch, 2014;Ritzi, 2000) and object-based methods (Deutsch and Tran, 2002;Maharaja, 2008;Pyrcz et al, 2009) are not effective at reproducing anisotropic features and connectivity patterns properly (Heinz et al, 2003;Klise et al, 2009;Knudby and Carrera, 2005;Vassena et al, 2010) due to the lack of high-order statistics and the difficulty in parameterization. To overcome the abovementioned limitations, multiple-point statistics (MPS) was developed over recent years and has shown prospects in modeling subsurface anisotropic structures, such as porous media, hydrofacies, reservoirs and other sedimentary structures (Dell Arciprete et al, 2012;Hajizadeh et al, 2011;Hu and Chugunova, 2008;Oriani et al, 2014;Pirot et al, 2015;Wu et al, 2006).…”

Response to Reviewer #1

“…5 No matter which MPS algorithm is used, a suitable training image is the fundamental requirement. Although such algorithms are gaining popularity in hydrogeological applications (Hermans et al, 2015;He et al, 2014;Høyer et al, 2017;Hu and Chugunova, 2008;Huysmans et al, 2014;Jha et al, 2014;Mahmud et al, 2015), they still suffer from one vital limitation: the lack of training images, especially for 3-D situations. Object-based or process-based techniques are one possibility to build 3-D training images (de Marsily et al, 2005;de Vries et al, 2009;Feyen and Caers, 2004;Maharaja, 10 2008;Pyrcz et al, 2009).…”