2018
DOI: 10.3390/w10070844
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Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

Abstract: Abstract:A large borehole lithology dataset from the shallowly buried alluvial aquifer of the Brenta River Megafan (NE Italy) is used in this paper to model hydrofacies with three classical geostatistical methods, namely the Object-Based Simulation (OBS), the Sequential Indicator Simulation (SIS), and the Truncated Gaussian Simulation (TGS), and rank alternative output models. Results show that, though compromising with geological realism and rendering a noisy picture of subsurface geology, the pixel-based TGS… Show more

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Cited by 13 publications
(9 citation statements)
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“…McKenna and Smith, 2004;Larue and Friedmann, 2005;Larue and Hovadik, 2006;Hovadik and Larue, 2007;Liu and Atan, 2008;de Jager et al, 2009;Burns et al, 2010;Jha et al, 2016;Peter et al, 2017). Realistic reproduction of sandbody connectivity in fluvial aquifers and reservoirs is crucial to several applications, for example when predictions need to be made relating to the efficiency of hydrocarbon production, the behavior of contaminant plumes, spatial variability in groundwater drawdown, the viability of programs of carbon capture and storage, or optimal geothermal exploitation of hot sedimentary aquifers (e.g., Keogh et al, 2007;Ronayne et al, 2008;Giambastiani et al, 2012;Norden and Frykman, 2013;Issautier et al, 2014;Jeong and Srinivasan, 2016;Nguyen et al, 2017;Willems et al, 2017;Marini et al, 2018). In this regard it is significant that object-based models are considered inherently realistic, thanks to their ability to reproduce complex predefined geometries and to leverage on analog data.…”
Section: Introductionmentioning
confidence: 99%
“…McKenna and Smith, 2004;Larue and Friedmann, 2005;Larue and Hovadik, 2006;Hovadik and Larue, 2007;Liu and Atan, 2008;de Jager et al, 2009;Burns et al, 2010;Jha et al, 2016;Peter et al, 2017). Realistic reproduction of sandbody connectivity in fluvial aquifers and reservoirs is crucial to several applications, for example when predictions need to be made relating to the efficiency of hydrocarbon production, the behavior of contaminant plumes, spatial variability in groundwater drawdown, the viability of programs of carbon capture and storage, or optimal geothermal exploitation of hot sedimentary aquifers (e.g., Keogh et al, 2007;Ronayne et al, 2008;Giambastiani et al, 2012;Norden and Frykman, 2013;Issautier et al, 2014;Jeong and Srinivasan, 2016;Nguyen et al, 2017;Willems et al, 2017;Marini et al, 2018). In this regard it is significant that object-based models are considered inherently realistic, thanks to their ability to reproduce complex predefined geometries and to leverage on analog data.…”
Section: Introductionmentioning
confidence: 99%
“…The thin, leaky confining unit at Oakland is detectable in the pumping test data, but resistivity-depth profiles do not provide any evidence of this layer. Borehole logs and pumping test data are necessary to resolve these vertical details, and integrating these data with AEM through geostatistical modeling presents a promising avenue for future research [37,38].…”
Section: Discussionmentioning
confidence: 99%
“…Categorical rather than continuous random fields can be considered in order to represent the distributions of facies. For this purpose, Sequential Indicator Simulation (SISIM; Goovaerts 1997;Deutsch and Journel 1998), which relies on indicator variograms (inferred, e.g., from borehole lithological data), is one of the most extensively applied methods (Deutsch 2006;Felletti et al 2006;Marini et al 2019). While being characterized by ease of implementation, this method may not honor volumetric proportions of facies, as inferred from available data, because it tends to underestimate less-prevalent facies (Emery 2004;He et al 2009), and/or may not fully capture patterns of facies connectivity (Gomez-Hernandez and Kerrou et al 2008), an issue which is particularly noticeable in three-dimensional systems (Dell'Arciprete et al 2012).…”
Section: Introductionmentioning
confidence: 99%
“…Increasing efforts have been devoted to investigate the effects of various geostatistical approaches on connectivity metrics (Bakshevskaia and Pozdniakov 2016;Dell'Arciprete et al 2012Lee et al 2007;Mohammadi et al 2020;Sharifzadehlari et al 2018;Vassena et al 2010). Other studies addressed the impact different geostatistical methods on the accuracy of estimated facies distribution (He et al 2009, Kessler et al 2013Marini et al 2019;Park et al 2007), hydraulic head and flux fields (Lee et al 2007;Bianchi et al 2015), or spreading of dissolved chemicals migrating in aquifer systems (Maghrebi et al 2015;Siirila-Woodburn and Maxwell 2015).…”
Section: Introductionmentioning
confidence: 99%