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 Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Yi Du ◽  
Jie Chen ◽  
Ting Zhang
Keyword(s):  
Porous Media ◽  
Transfer Learning ◽  
High Efficiency ◽  
Three Dimensional ◽  
Fluid Flows ◽  
Training Image ◽  
Multiple Point ◽  
Reconstruction Methods ◽  
Engineering Sciences ◽  
Complex Features

The reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. The proposed method was evaluated on shale and sandstone samples by comparing multiple-point connectivity functions, variogram curves, permeability, porosity, etc. The experimental results show that the proposed method is of high efficiency while preserving similar features with the target image, shortening reconstruction time, and reducing the burdens on CPU.

scholarly journals Quantitative Analysis for the Reconstruction of Porous Media Using Multiple-Point Statistics

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Qing Xie ◽  
Jianping Xu ◽  
Yuanda Yuan ◽  
Cong Niu
Keyword(s):  
Porous Media ◽  
Large Scale ◽  
Pore Space ◽  
Flow In Porous Media ◽  
Template Method ◽  
Multiple Point ◽  
Multiple Point Statistics ◽  
Grid Scheme ◽  
The Impact ◽  
Data Template

The pore structure reconstruction of the porous media is of great importance to the research of mechanisms of fluid flow in porous media. To capture the large-scale patterns in the pore space, the multiple-point statistical technique is generally adopted for porous media reconstruction. Commonly, two different schemes, i.e., the single-grid scheme and the multiple-grid scheme, can be applied for simulation realization. The selection between this two schemes and a proper data template size have thus become a new research issue, and the performance of the characteristic reproduction of the training image using this two schemes must be quantified. In this paper, a series of multiple-point statistics simulation basing on a 2D micro-CT sandstone image are proceeded using both single- and multiple-grid schemes, and different data templates are adapted for porous media reconstruction. Further, to quantify the impact of the computational schemes and setting of the data template to the simulation realizations, a number of measurements considering the pore diameter, porosity, connectivity, and permeability are implemented to fully analyze the results obtained. Results show that by using the single-point statistical method, a large template is necessary to reproduce large-scale structures. The multiple-grid template method may bring great benefits to simulation efficiency over the simple data template method, as well as the recovery of the pore long-range geometric features and seepage characteristics. With the extension of the template for the multiple-grid scheme, the simulation results show lack of variations to some extent.

Reconstruction of 3D Porous Media Using Multiple-Point Statistics Based on a 2D Training Image

2018 ◽  
Author(s):  
Yuqi Wu ◽  
Chengyan Lin ◽  
Lihua Ren ◽  
Weichao Tian ◽  
Yang Wang ◽  
...  
Keyword(s):  
Porous Media ◽  
Training Image ◽  
Multiple Point ◽  
Multiple Point Statistics

Reconstruction of 3D porous media using multiple-point statistics based on a 3D training image

2018 ◽  
Vol 51 ◽  
pp. 129-140 ◽  
Author(s):  
Yuqi Wu ◽  
Chengyan Lin ◽  
Lihua Ren ◽  
Weichao Yan ◽  
Senyou An ◽  
...  
Keyword(s):  
Porous Media ◽  
Training Image ◽  
Multiple Point ◽  
Multiple Point Statistics

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.

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.

scholarly journals A unified modelling and simulation for coupled anomalous transport in porous media and its finite element implementation

2021 ◽  
Author(s):  
O. Barrera
Keyword(s):  
Finite Element ◽  
Porous Media ◽  
Diffusion Equation ◽  
Pore Pressure ◽  
Large Scale ◽  
Anomalous Transport ◽  
Computational Framework ◽  
Transient Phenomena ◽  
Large Scale Structures ◽  
Pressure Diffusion

AbstractThis paper presents an unified mathematical and computational framework for mechanics-coupled “anomalous” transport phenomena in porous media. The anomalous diffusion is mainly due to variable fluid flow rates caused by spatially and temporally varying permeability. This type of behaviour is described by a fractional pore pressure diffusion equation. The diffusion transient phenomena is significantly affected by the order of the fractional operators. In order to solve 3D consolidation problems of large scale structures, the fractional pore pressure diffusion equation is implemented in a finite element framework adopting the discretised formulation of fractional derivatives given by Grunwald–Letnikov (GL). Here the fractional pore pressure diffusion equation is implemented in the commercial software Abaqus through an open-source UMATHT subroutine. The similarity between pore pressure, heat and hydrogen transport is also discussed in order to show that it is possible to use the coupled thermal-stress analysis to solve fractional consolidation problems.

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.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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