Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments

Dell’Arciprete, D.; Bersezio, R.; Felletti, Fabrizio; Giudici, M.; Comunian, Alessandro; Renard, Philippe

doi:10.1007/s10040-011-0808-0

Cited by 93 publications

(43 citation statements)

References 46 publications

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“…Results of this study confirm some of the well-known limitations of classical geostatistical algorithms in replicating the lithofacies heterogeneity of channelized alluvial sediments [1,2,[11][12][13][14]37,38]. However, it was possible to rank the tested algorithms based on how closely they honored input data and predicted facies at validation boreholes [11], as well as they replicated the current hydrostratigraphic model of the study area [26].…”

Section: Which Modelling Algorithm Does Better With Lithology From Desupporting

confidence: 57%

“…However, because the scale of heterogeneity of alluvial lithofacies can be much finer than typical inter-borehole spacing [1][2][3][4][5], understanding how K varies in space from sparse hydraulic tests is inherently impractical. Alternatively, when numerous continuous-core boreholes are available, one can resort to geostatistics for simulating lithofacies at first step, and then obtain the K-field by assigning appropriate K to simulated lithofacies [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

Marini

Felletti

Beretta

et al. 2018

Water

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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 and SIS are better suited than OBS for their ease of conditioning to closely spaced boreholes, especially in fine-scale simulation grids. In turn, SIS appears to provide better prediction and less noisy hydrofacies models than TGS without requiring assumptions about relationship among operative facies, which makes it particularly suited for use with large borehole lithology datasets lacking detail and quality consistency. Flow simulation on a test volume constrained with numerous boreholes indicates the SIS hydrofacies models feature well-connected sands forming relatively fast flow paths as opposed to TGS models, which instead appear to carry a more dispersed flow. It is shown how such a difference primarily relates to 'noise', which in TGS models is so widespread to translate into a disordered spatial distribution of K and, consequently, a nearly isotropic simulated flow.

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Section: Which Modelling Algorithm Does Better With Lithology From Desupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

Marini

Felletti

Beretta

et al. 2018

Water

Self Cite

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“…This hydraulic feature corresponds to that of open framework gravel (OFG), which is usually observed in outcrops or trenches, and is considered to be the most remarkable hydro-face due to its high permeability. OFG has a distribution that is only centimeters or decimeters thick, but its K value is of the order of 1 × 10 -3 to 1 × 10 -2 m/s, considerably greater than the value for the surrounding layers (Jussel et al 1994;Heinz 2003;Lunt et al 2004;Zappa et al 2006;Ferreira et al 2010;dell'Arciprete et al 2012).…”

Section: Relation Between Slug Tests and Core Propertiesmentioning

confidence: 90%

Dépendance à la profondeur et modèles exponentiels de perméabilité dans des cônes de déjection graveleux

Sakata

Ikeda

2013

Hydrogeol J

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Abstract:To determine depth dependence of permeability in various geologic deposits, exponential models have often been proposed. However, spatial variability in hydraulic conductivity, K, rarely fits this trend in coarse alluvial aquifers, where complex stratigraphic sequences follow unique trends due to depositional and post-depositional processes. This paper analyzes K of alluvial-fan gravel deposits in several boreholes, and finds exponential decay in K with depth. Relatively undisturbed gravel cores obtained in the Toyohira River alluvial fan, Sapporo, Japan, are categorized by four levels of fine-sediment packing between gravel grains. Grain size is also analyzed in cores from two boreholes in the mid-fan and one in the fan-toe. Profiles of estimated conductivity, K , are constructed from profiles of core properties through a well-defined relation between slug-test results and core properties. Errors in K are eliminated by a moving-average method, and regression analysis provides the decay exponents of K with depth. Moving-average results show a similar decreasing trend in only the mid-fan above ~30-m depth, and the decay exponent is estimated as ≈0.11 m -1 , which is 10-to 1000-fold that in consolidated rocks. A longitudinal cross section is also generated by using the profiles to establish hydrogeologic boundaries in the fan.

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“…Lastly, the less abundant facies might be represented on a 20 m domain, but it will often not be visible on the 100 m domain chosen for the subsequent hydrological flow simulations. Dell'Arciprete et al (2012) present a study where geostatistics are implemented to simulate small-scale heterogeneities in a multi-facies environment.…”

Section: Tprogs Setupmentioning

confidence: 99%

Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft data set

Koch

Jensen

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. In traditional hydrogeological investigations, one geological model is often used based on subjective interpretations and sparse data availability. This deterministic approach usually does not account for any uncertainties. Stochastic simulation methods address this problem and can capture the geological structure uncertainty. In this study the geostatistical software TProGS is utilized to simulate an ensemble of realizations for a binary (sand/clay) hydrofacies model in the Norsminde catchment, Denmark. TProGS can incorporate soft data, which represent the associated level of uncertainty. High-density (20 m × 20 m × 2 m) airborne geophysical data (SkyTEM) and categorized borehole data are utilized to define the model of spatial variability in horizontal and vertical direction, respectively, and both are used for soft conditioning of the TProGS simulations. The category probabilities for the SkyTEM data set are derived from a histogram probability matching method, where resistivity is paired with the corresponding lithology from the categorized borehole data. This study integrates two distinct data sources into the stochastic modeling process that represent two extremes of the conditioning density spectrum: sparse borehole data and abundant SkyTEM data. In the latter the data have a strong spatial correlation caused by its high data density, which triggers the problem of overconditioning. This problem is addressed by a work-around utilizing a sampling/decimation of the data set, with the aim to reduce the spatial correlation of the conditioning data set. In the case of abundant conditioning data, it is shown that TProGS is capable of reproducing non-stationary trends. The stochastic realizations are validated by five performance criteria: (1) sand proportion, (2) mean length, (3) geobody connectivity, (4) facies probability distribution and (5) facies probability-resistivity bias.In conclusion, a stochastically generated set of realizations soft-conditioned to 200 m moving sampling of geophysical data performs most satisfactorily when balancing the five performance criteria. The ensemble can be used in subsequent hydrogeological flow modeling to address the predictive uncertainty originating from the geological structure uncertainty.

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Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments

Cited by 93 publications

References 46 publications

Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)

Dépendance à la profondeur et modèles exponentiels de perméabilité dans des cônes de déjection graveleux

Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft data set

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