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

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 A location-based multiple point statistics method: modelling the reservoir with non-stationary characteristics

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Yanshu Yin ◽  
Wenjie Feng
Keyword(s):  
Distance Function ◽  
Training Image ◽  
New Method ◽  
Central Position ◽  
Multiple Point ◽  
Delta Front ◽  
Multiple Point Statistics ◽  
Shengli Oilfield ◽  
Path Distance ◽  
The Relationship

AbstractIn this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central position distance function, which alleviates the requirement that the training image and the simulated grids have the same dimension. The weights in every direction of the distance function can be changed to characterize the reservoir heterogeneity in various directions. The local integral replacements of data events, structured random path, distance tolerance and multi-grid strategy are applied to reproduce the sedimentary patterns and obtain a more realistic result. This method is compared with the traditional Snesim method using a synthesized 3-D training image of Poyang Lake and a reservoir model of Shengli Oilfield in China. The results indicate that the new method can reproduce the non-stationary characteristics better than the traditional method and is more suitable for simulation of delta-front deposits. These results show that the new method is a powerful tool for modelling a reservoir with non-stationary characteristics.

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.

GPU-accelerated 3D reconstruction of porous media using multiple-point statistics

2014 ◽  
Vol 19 (1) ◽  
pp. 79-98 ◽  
Author(s):  
Ting Zhang ◽  
Yi Du ◽  
Tao Huang ◽  
Xue Li
Keyword(s):  
Porous Media ◽  
3D Reconstruction ◽  
Multiple Point ◽  
Multiple Point Statistics

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 3D Multiple-point Statistics Simulations of the Roussillon Continental Pliocene Aquifer using DeeSse

2020 ◽  
Author(s):  
Valentin Dall'Alba ◽  
Philippe Renard ◽  
Julien Straubhaar ◽  
Benoit Issautier ◽  
Cédric Duvail ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Auxiliary Information ◽  
Training Image ◽  
Multiple Point ◽  
Data Set ◽  
Multiple Point Statistics ◽  
Sedimentary Environments ◽  
Additional Information ◽  
Continuous Rotation ◽  
Conditioning Data

Abstract. This study presents a novel workflow to model the internal heterogeneity of complex aquifers using the multiple-point statistics algorithm DeeSse. We illustrate the applicability of this workflow on the Roussillon's aquifer in the region of Perpignan (southern France). This work is part of a project aiming at assessing the groundwater dynamics of this Mediterranean aquifer in the context of a growing population, climate change, and increasing pressure on the freshwater resources. We focus here on the geological heterogeneity of the Continental Pliocene layer because it is expected to influence possible saltwater intrusion process and its corresponding uncertainty quantification. The main aim of the paper is therefore to describe the procedure that is used to model the aquifer heterogeneity with a relatively small number of direct geological observations and a well defined geological concept. When few direct observations are available, the traditional geostatistical approaches cannot be applied easily because variogram inference is difficult. On the opposite, multiple-point statistics simulations can rely on a conceptual geological model. Here, the conceptual model consists not only of a training image displaying the spatial organization of the main sedimentological elements in space, but also in a set of additional information such as general trends and paleo orientations of the sedimentological features. The direct sampling algorithm DeeSse can be used in this context to model the expected heterogeneity. The workflow involves creating 2D non-stationary training images (TI) coupled during simulation with auxiliary information and controlled by hard conditioning data obtained from interpreted electrofacies. 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 source of the sediments to the outlet of the system. A 3D continuous rotation map is estimated from 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 two 2D simulations is controlled by both the hard conditioning data set and by simulating conditional data points from one simulation to another. This process allows to bypass the creation of a 3D training image while preserving the vertical continuity of the sedimentary objects.

Multiple-Point Statistics for Training Image Selection

2007 ◽  
Vol 16 (4) ◽  
pp. 313-321 ◽  
Author(s):  
Jeff B. Boisvert ◽  
Michael J. Pyrcz ◽  
Clayton V. Deutsch
Keyword(s):  
Training Image ◽  
Multiple Point ◽  
Multiple Point Statistics

scholarly journals Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections

2018 ◽  
Vol 22 (12) ◽  
pp. 6547-6566 ◽  
Author(s):  
Qiyu Chen ◽  
Gregoire Mariethoz ◽  
Gang Liu ◽  
Alessandro Comunian ◽  
Xiaogang Ma
Keyword(s):  
Cross Sections ◽  
Three Dimensional ◽  
Training Image ◽  
Sensitivity Analyses ◽  
Multiple Point ◽  
Multiple Point Statistics ◽  
Training Images ◽  
Critical Issues ◽  
Probability Aggregation ◽  
Novel Strategy

Abstract. Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. For a practical three-dimensional (3-D) application, however, one of the critical issues is the difficulty in obtaining a credible 3-D training image. However, bidimensional (2-D) training images are often available because established workflows exist to derive 2-D sections from scattered boreholes and/or other samples. In this work, we propose a locality-based MPS approach to reconstruct 3-D geological models on the basis of such 2-D cross sections (3DRCS), making 3-D training images unnecessary. Only several local training subsections closer to the central uninformed node are used in the MPS simulation. The main advantages of this partitioned search strategy are the high computational efficiency and a relaxation of the stationarity assumption. We embed this strategy into a standard MPS framework. Two probability aggregation formulas and their combinations are used to assemble the probability density functions (PDFs) from different subsections. Moreover, a novel strategy is adopted to capture more stable PDFs, where the distances between patterns and flexible neighborhoods are integrated on multiple grids. A series of sensitivity analyses demonstrate the stability of the proposed approach. Several hydrogeological 3-D application examples illustrate the applicability of the 3DRCS approach in reproducing complex geological features. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.

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