Applied Geostatistics for Reservoir Characterization

Kelkar, Mohan; Perez, Godofredo

doi:10.2118/9781555630959

Cited by 123 publications

(22 citation statements)

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“…Variogram modelling is a fitting of calculated variogram by a model from mathematical equations. The common models are Gaussian, Exponential, Spherical, and Nugget models [3] (Fig. 1).…”

Section: Theory and Methods 21 Krigingmentioning

confidence: 99%

“…Geostatistics is a branch of statistics that focuses on spatial estimation of spatially correlated variables for earth science applications and their uncertainty [3], [4]. Some of the geostatistics methods are linear kriging, nonlinear kriging, co-kriging, simulation [3], and multi-point geostatistics [5]. Kriging is a common and widely used method to create a risk map.…”

Section: Introductionmentioning

confidence: 99%

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Geostatistics for Risk Level Mapping: Synthetic Data Example

Pranowo¹,

Ramadhani²

2022

Proceedings of the 1st International Conference on Contemporary Risk Studies, ICONIC-RS 2022, 31 March-1 April 2022, South Jaka

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Risk level mapping is important for mitigation plans and any disaster-related decision. However, the data in certain areas can be sparse for some reasons. The risk levels are not known or analyzed at some positions. In statistics, these positions are called unsampled locations. Geostatistics can play a role in estimating the risk at unsampled locations. The most common geostatistics method that can be used is kriging. Moreover, kriging can calculate the uncertainty of its estimation. This paper aims to investigate the benefit of kriging in risk level mapping. The synthetic data experiment is conducted to explain how kriging works in risk level mapping. Kriging method is able to estimate the risk level at unsampled locations and take into account uncertainties.

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“…Variogram modelling is a fitting of calculated variogram by a model from mathematical equations. The common models are Gaussian, Exponential, Spherical, and Nugget models [3] (Fig. 1).…”

Section: Theory and Methods 21 Krigingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geostatistics for Risk Level Mapping: Synthetic Data Example

Pranowo¹,

Ramadhani²

2022

Proceedings of the 1st International Conference on Contemporary Risk Studies, ICONIC-RS 2022, 31 March-1 April 2022, South Jaka

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“…We assume that the stochastic variability of subsurface porosity can be adequately captured by a Gaussian two-point geostatistical model. This assumption is generally considered to be valid for a given hydrogeological unit (e.g., Kelkar and Perez, 2002;Dafflon et al, 2009). For the parameterization of this model, we consider the so-called von Kármán autocorrelation function which, due to its versatility, has been used for a wide variety of research objectives, such as seafloor morphology quantification (e.g., Goff and Jordan, 1988), borehole data analysis (e.g., Dolan and Bean, 1997;Jones and Holliger, 1997), numerical simulations of wave propagation (e.g., Frankel and Clayton, 1986;Hartzell et al, 2010), and aquifer characterization (e.g., Tronicke and Holliger, 2005;Dafflon et al, 2009).…”

Section: Estimation Of Subsurface Stochastic Parametersmentioning

confidence: 99%

Conditional stochastic inversion of common-offset ground-penetrating radar reflection data

Irving

et al. 2021

GEOPHYSICS

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We present a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile, or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that the proposed method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

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“…The interpolation of geological and geophysical observations is a common problem in the geosciences with applications in mineral exploration (Journel and Huijbregts, 1976;Emery and Maleki, 2019), oil reservoir modeling (Kelkar and Perez, 2002;Pyrcz and Deutsch, 2014), groundwater hydrology (Kitanidis, 1997;Feyen and Caers, 2006), and soil sciences (Goovaerts, 1999;Lark, 2012;Xiong et al, 2015). The field of geostatistics emerged in the 1950s with the development of kriging, a method for optimizing spatial interpolation, which was originally used to estimate gold ore grades (Krige, 1951;Cressie, 1990).…”

Section: Introductionmentioning

confidence: 99%

GStatSim V1.0: a Python package for geostatistical interpolation and simulation

MacKie

Field

Wang

et al. 2022

Preprint

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Abstract. The interpolation of geospatial phenomena is a common problem in Earth sciences applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability of measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistics tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland.

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Applied Geostatistics for Reservoir Characterization

Cited by 123 publications

References 0 publications

Geostatistics for Risk Level Mapping: Synthetic Data Example

Geostatistics for Risk Level Mapping: Synthetic Data Example

Conditional stochastic inversion of common-offset ground-penetrating radar reflection data

GStatSim V1.0: a Python package for geostatistical interpolation and simulation

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