scholarly journals Contributions to uncertainty related to hydrostratigraphic modeling using Multiple-Point Statistics

2018 ◽  
Author(s):  
Adrian A. S. Barfod ◽  
Troels N. Vilhelmsen ◽  
Flemming Jørgensen ◽  
Anders V. Christiansen ◽  
Julien Straubhaar ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Training Image ◽  
Multiple Point ◽  
Geological Model ◽  
Soft Data ◽  
Borehole Data ◽  
Subsurface Geology ◽  
The Impact ◽  
Neighboring Area

Abstract. Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art Multiple-Point Statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted Denmark using the snesim algorithm. The goal is to study how changes to the underlying datasets 5 propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the Training Image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually, as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried 10 out exhaustively, and realizations are compared with each other as well as with the cognitive geological model. The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology or TI, results in a significant increase in realization uncertainty. The SkyTEM data used in this study cover a large portion of the Kasted model area, and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparse, and only 410 boreholes were present in this study. The borehole lithology logs 15 infer local changes in the immediate vicinity of the boreholes, thus providing limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. Finally, the importance of the TI was studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological setting are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is 20 used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

scholarly journals Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

2018 ◽  
Vol 22 (10) ◽  
pp. 5485-5508 ◽  
Author(s):  
Adrian A. S. Barfod ◽  
Troels N. Vilhelmsen ◽  
Flemming Jørgensen ◽  
Anders V. Christiansen ◽  
Anne-Sophie Høyer ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Training Image ◽  
Multiple Point ◽  
Geological Model ◽  
Soft Data ◽  
Borehole Data ◽  
Transient Electromagnetic ◽  
High Degree ◽  
The Impact

Abstract. Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art multiple-point statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted, Denmark, using the snesim algorithm. The goal is to study how changes to the underlying datasets propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the training image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried out exhaustively, and realizations are compared with each other as well as with the cognitive geological model. The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology, or TI, results in a significant increase in realization uncertainty. The airborne transient electromagnetic SkyTEM data used in this study cover a large portion of the Kasted model area and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparser, and 410 boreholes were present in this study. The borehole lithology logs infer local changes in the immediate vicinity of the boreholes, thus, in areas with a high degree of geological heterogeneity, boreholes only provide limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. The importance of the TI was also studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological settings are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

The MPS Reconstruction of Porous Media Using Multiple-Grid Templates

2013 ◽  
Vol 462-463 ◽  
pp. 462-465 ◽  
Author(s):  
Yi Du ◽  
Ting Zhang
Keyword(s):  
Porous Media ◽  
Large Scale ◽  
Grid Method ◽  
Training Image ◽  
Multiple Point ◽  
Grid Data ◽  
Large Scale Structures ◽  
Complex Features ◽  
Grid Nodes ◽  
Data Template

It is difficult to reconstruct the unknown information only by some sparse known data in the reconstruction of porous media. Multiple-point geostatistics (MPS) has been proved to be a powerful tool to capture curvilinear structures or complex features in training images. One solution to capture large-scale structures while considering a data template with a reasonably small number of grid nodes is provided by the multiple-grid method. This method consists in scanning a training image using increasingly finer multiple-grid data templates instead of a big and dense data template. The experimental results demonstrate that multiple-grid data templates and MPS are practical in porous media reconstruction.

scholarly journals Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time

2013 ◽  
Vol 10 (3) ◽  
pp. 2789-2833 ◽  
Author(s):  
X. He ◽  
T. O. Sonnenborg ◽  
F. Jørgensen ◽  
A.-S. Høyer ◽  
R. Roende Møller ◽  
...  
Keyword(s):  
Groundwater Modeling ◽  
Groundwater Age ◽  
Multiple Point ◽  
Hydraulic Parameters ◽  
Geological Model ◽  
Groundwater Model ◽  
Geological Uncertainty ◽  
Groundwater Head ◽  
Geological Models ◽  
The Impact

Abstract. Uncertainty of groundwater model predictions has in the past mostly been related to uncertainty in the hydraulic parameters whereas uncertainty in the geological structure has not been considered to the same extent. Recent developments in theoretical methods for quantifying geological uncertainty have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS) integrated in the Stanford Geostatistical Modeling Software (SGeMS) for exploring the impact of geological uncertainty on groundwater flow patterns for a site in Denmark. Realizations from the geostatistical model were used as input to a groundwater model developed from MODFLOW within the GMS modeling environment. The uncertainty analysis was carried out in three scenarios involving simulation of groundwater head distribution and groundwater age. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model optimization where the hydraulic parameters for each of them were estimated by calibration against observations of hydraulic head and stream discharge. In the third scenario each geological model was run with 216 randomized set of parameters. The analysis documented that the uncertainty on the conceptual geological model was as significant as the uncertainty related to the embedded hydraulic parameters.

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

2014 ◽  
Vol 18 (8) ◽  
pp. 2907-2923 ◽  
Author(s):  
J. Koch ◽  
X. He ◽  
K. H. Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Spatial Correlation ◽  
Geological Structure ◽  
Geophysical Data ◽  
Data Availability ◽  
Performance Criteria ◽  
Geological Model ◽  
Soft Data ◽  
Borehole Data ◽  
Data Set ◽  
Conditioning Data

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.

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

2017 ◽  
Vol 21 (12) ◽  
pp. 6069-6089 ◽  
Author(s):  
Anne-Sophie Høyer ◽  
Giulio Vignoli ◽  
Thomas Mejer Hansen ◽  
Le Thanh Vu ◽  
Donald A. Keefer ◽  
...  
Keyword(s):  
Development Strategy ◽  
Large Scale ◽  
Statistical Simulation ◽  
Training Image ◽  
Multiple Point ◽  
Data Sets ◽  
Input Information ◽  
Training Images ◽  
Voxel Model ◽  
Novel Strategy

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 km2 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 km2. 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.

Regional aquifer mapping using combined VES-TEM-AMT/EMAP methods in the semiarid eastern margin of Parnaiba Basin, Brazil

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 337-356 ◽  
Author(s):  
M. A. Meju ◽  
S. L. Fontes ◽  
M. F. B. Oliveira ◽  
J. P. R. Lima ◽  
E. U. Ulugergerli ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Integration Scheme ◽  
Data Sets ◽  
Borehole Data ◽  
Eastern Margin ◽  
Transient Electromagnetic ◽  
Hydrogeological Studies ◽  
East West ◽  
Parnaíba Basin

As part of a program to evaluate the utility of integrated geoelectrical methods for subsurface structural mapping and groundwater resource investigation in the semiarid eastern margin of the Parnaiba basin in Brazil, several vertical electrical soundings (VES) and transient electromagnetic (TEM) and tensorial audiomagnetotelluric (AMT) measurements were carried out along a 250-km-long east‐west transect (passing through major towns and cities) and a 127-km-long north‐south profile (passing through small farm settlements). The various data sets have been jointly processed using a novel integration scheme and a constrained 1-D inversion technique to yield the resistivity structure underneath each observation station. Regularized 2-D inversion of static‐shift‐corrected, dual‐mode AMT data provided additional deep structural information and, together with the joint 1-D results, enabled an assessment of the distribution of aquifers and major structural controls in the region. The east‐west regional geoelectrical model evokes a picture of a gently dipping succession of conductive and resistive units in good agreement with the alternating shaly and sandy formations evinced from preexisting borehole data and previous geological studies. The geoelectric models also show the presence of the large‐scale Transbrazilian lineament and other graben‐like structures, previously inferred from aeromagnetic data, which may have some control on groundwater distribution. The agreement with geology and aeromagnetic interpretation emphasizes the importance of integrated geoelectrical surveying as a complementary or independent means of obtaining useful stratigraphic and structural information for hydrogeological studies in this region.

scholarly journals Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft dataset

2013 ◽  
Vol 10 (12) ◽  
pp. 15219-15262 ◽  
Author(s):  
J. Koch ◽  
X. He ◽  
K. H. Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Geological Structure ◽  
High Density ◽  
Geophysical Data ◽  
Data Availability ◽  
Performance Criteria ◽  
Geological Model ◽  
Soft Data ◽  
Borehole Data ◽  
Density Spectrum ◽  
Spatially Correlated

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 and for soft conditioning the TProGS simulations. The category probabilities for the SkyTEM dataset are derived from a histogram probability matching method, where resistivity is paired with the corresponding lithology from the categorized borehole data. A novelty of this study is the incorporation of two distinct datasources into the stochastic modeling process that represents two extremes of the conditioning density spectrum; sparse borehole data and abundant SkyTEM data. The high density of spatially correlated SkyTEM data lead to very deterministic simulation results. This is caused by overconditioning and addressed by a work around utilizing a resampling (thinning) of the dataset. 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. As conclusion, a stochastically generated set of realizations soft conditioned to 200 m moving sampling of geophysical data performs most satisfying when balancing the five performance criteria and can be used in subsequent hydrogeological flow modeling to address the predictive uncertainty originated from the geological structure uncertainty.

scholarly journals Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods

2018 ◽  
Vol 22 (6) ◽  
pp. 3351-3373 ◽  
Author(s):  
Adrian A. S. Barfod ◽  
Ingelise Møller ◽  
Anders V. Christiansen ◽  
Anne-Sophie Høyer ◽  
Júlio Hoffimann ◽  
...  
Keyword(s):  
Computation Time ◽  
Training Image ◽  
Search Tree ◽  
Multiple Point ◽  
Soft Data ◽  
Test Environment ◽  
Near Surface ◽  
Multiple Point Statistics ◽  
Advantages And Disadvantages ◽  
Transient Electromagnetic

Abstract. Creating increasingly realistic groundwater models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modeling procedure. Using multiple-point statistics (MPS) for stochastic hydrostratigraphic modeling provides a degree of flexibility that allows the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modeling. This paper focuses on comparing three MPS methods: snesim, DS and iqsim. The MPS methods are tested and compared on a real-world hydrogeophysical survey from Kasted in Denmark, which covers an area of 45 km2. A controlled test environment, similar to a synthetic test case, is constructed from the Kasted survey and is used to compare the modeling results of the three aforementioned MPS methods. The comparison of the stochastic hydrostratigraphic MPS models is carried out in an elaborate scheme of visual inspection, mathematical similarity and consistency with boreholes. Using the Kasted survey data, an example for modeling new survey areas is presented. A cognitive hydrostratigraphic model of one area is used as a training image (TI) to create a suite of stochastic hydrostratigraphic models in a new survey area. The advantage of stochastic modeling is that detailed multiple point information from one area can be easily transferred to another area considering uncertainty. The presented MPS methods each have their own set of advantages and disadvantages. The DS method had average computation times of 6–7 h, which is large, compared to iqsim with average computation times of 10–12 min. However, iqsim generally did not properly constrain the near-surface part of the spatially dense soft data variable. The computation time of 2–3 h for snesim was in between DS and iqsim. The snesim implementation used here is part of the Stanford Geostatistical Modeling Software, or SGeMS. The snesim setup was not trivial, with numerous parameter settings, usage of multiple grids and a search-tree database. However, once the parameters had been set it yielded comparable results to the other methods. Both iqsim and DS are easy to script and run in parallel on a server, which is not the case for the snesim implementation in SGeMS.

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

2016 ◽  
Author(s):  
Anne-Sophie Høyer ◽  
Giulio Vignoli ◽  
Thomas Mejer Hansen ◽  
Le Thanh Vu ◽  
Donald A. Keefer ◽  
...  
Keyword(s):  
Development Strategy ◽  
Large Scale ◽  
Statistical Simulation ◽  
Training Image ◽  
Multiple Point ◽  
Data Sets ◽  
Input Information ◽  
Training Images ◽  
Voxel Model ◽  
Novel Strategy

Abstract. Most studies about 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 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D 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 km2 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 3D 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 sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

scholarly journals 3D multiple-point statistics simulations of the Roussillon Continental Pliocene aquifer using DeeSse

2020 ◽  
Vol 24 (10) ◽  
pp. 4997-5013
Author(s):  
Valentin Dall'Alba ◽  
Philippe Renard ◽  
Julien Straubhaar ◽  
Benoit Issautier ◽  
Cédric Duvail ◽  
...  
Keyword(s):  
Sampling Strategy ◽  
Spatial Arrangement ◽  
Training Image ◽  
Multiple Point ◽  
Spatial Evolution ◽  
Borehole Data ◽  
Geological Structures ◽  
Efficient Manner ◽  
Multiple Point Statistics ◽  
Continuous Rotation

Abstract. This study introduces a novel workflow to model the heterogeneity of complex aquifers using the multiple-point statistics algorithm DeeSse. We illustrate the approach by modeling the Continental Pliocene layer of the Roussillon aquifer in the region of Perpignan (southern France). When few direct observations are available, statistical inference from field data is difficult if not impossible and traditional geostatistical approaches cannot be applied directly. By contrast, multiple-point statistics simulations can rely on one or several alternative conceptual geological models provided using training images (TIs). But since the spatial arrangement of geological structures is often non-stationary and complex, there is a need for methods that allow to describe and account for the non-stationarity in a simple but efficient manner. The main aim of this paper is therefore to propose a workflow, based on the direct sampling algorithm DeeSse, for these situations. The conceptual model is provided by the geologist as a 2D non-stationary training image in map view displaying the possible organization of the geological structures and their spatial evolution. To control the non-stationarity, a 3D trend map is obtained by solving numerically the diffusivity equation as a proxy to describe the spatial evolution of the sedimentary patterns, from the sources of the sediments to the outlet of the system. A 3D continuous rotation map is estimated from inferred paleo-orientations of the fluvial system. Both trend and orientation maps are derived from geological insights gathered from outcrops and general knowledge of processes occurring in these types of sedimentary environments. Finally, the 3D model is obtained by stacking 2D simulations following the paleo-topography of the aquifer. The vertical facies transition between successive 2D simulations is controlled partly by the borehole data used for conditioning and by a sampling strategy. This strategy accounts for vertical probability of transitions, which are derived from the borehole observations, and works by simulating a set of conditional data points from one layer to the next. This process allows us to bypass the creation of a 3D training image, which may be cumbersome, while honoring the observed vertical continuity.

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