通过多点统计法采用垂直非平稳性模拟真实世界的埋藏河谷

He, X.; Sonnenborg, Torben O.; Jørgensen, Flemming; Jensen, Karsten H.

doi:10.1007/s10040-016-1486-8

Cited by 9 publications

(11 citation statements)

References 36 publications

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“…An option would be to use these resistivity data in the MPS modelling as soft conditioning data, such that high electric resistivities indicate sand, while clay corresponds to low resistivity values (see, for instance, He et al, 2014He et al, , 2016. However, EM data have limited resolution capability towards thin layers (Ley-Cooper et al, 2014;Vignoli et al, 2017), especially in the deeper parts of the investigated sequences.…”

Section: Discussionmentioning

confidence: 99%

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

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of 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 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D 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 training image is constructed as a relatively small 3-D 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 training image 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 training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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Section: Discussionmentioning

confidence: 99%

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

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

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“…In theory, MPS implementations assume stationary TIs (Y. and MPS application to strongly non-stationary systems is currently an area of research (e.g. Honarkhah and Caers, 2012;Straubhaar et al, 2011;de Vries et al, 2009;Chugunova et al, 2008). The TI used for this study was generated by following the unique criterion that the unconditioned simulation (obtained by using SNESIM through its implementation in SGeMS) could satisfactorily reproduce the expected geological structures.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the data already incorporated as (soft and hard) conditioning data in this study, dense electromagnetic (EM) surveys on approximately half of the area are available in the Danish geophysical database Gerda (Møller et al, 2009). An option would be to use these resistivity data in the MPS modelling as soft conditioning data, such that high electric resistivities indicate sand, while clay corresponds to low resistivity values (see, for instance, He et al, 2014He et al, , 2016. However, EM data have limited resolution capability towards thin layers (Ley-Cooper et al, 2014;Vignoli et al, 2017), especially in the deeper parts of the investigated sequences.…”

Section: Discussionmentioning

confidence: 99%

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Vignoli¹

2017

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Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of 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 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D 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 training image is constructed as a relatively small 3-D 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 training image 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 training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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“…An example of a trickle-down effect is how dell'Arcipreti et al ( 2012) becomes a main reference in He et al (2017) for promoting application of mps, then (He et al, 2017) becomes a main reference in Langousis et al (2018) for criticizing the 3-D Markov chain modeling approach of Carle and Fogg (1997). To "reveal" limitations of Markov chain models, Langousis et al (2018) execute a "simple test-case" of a 2-D dipping layer with a coordinate system anchored in the vertical and horizontal directions of their statistical analysis.…”

Section: Methods Comparisonmentioning

confidence: 99%

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Carle

Fogg

2020

Front. Earth Sci.

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Uncertain or indirect "soft" data, such as geologic interpretation, driller's logs, geophysical logs or imaging, offer potential constraints or "soft conditioning" to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data "hardness" parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or "prior realization") can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

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通过多点统计法采用垂直非平稳性模拟真实世界的埋藏河谷

Cited by 9 publications

References 36 publications

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

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

Reply to Reviewer #1

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

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