scholarly journals Challenges and Prospects of Digital Core-Reconstruction Research

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-29 ◽  
Author(s):  
Linqi Zhu ◽  
Chong Zhang ◽  
Chaomo Zhang ◽  
Xueqing Zhou ◽  
Zhansong Zhang ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Three Dimensional ◽  
Morphological Characteristics ◽  
Research Area ◽  
Rock Properties ◽  
Reconstruction Method ◽  
Oil And Gas Exploration ◽  
Stochastic Reconstruction ◽  
Reconstruction Methods ◽  
Digital Core

The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.

scholarly journals Reconstructing intragranular strain fields in polycrystalline materials from scanning 3DXRD data

2020 ◽  
Vol 53 (2) ◽  
pp. 314-325 ◽  
Author(s):  
N. Axel Henningsson ◽  
Stephen A. Hall ◽  
Jonathan P. Wright ◽  
Johan Hektor
Keyword(s):  
Three Dimensional ◽  
Polycrystalline Materials ◽  
Reconstruction Method ◽  
X Ray Diffraction ◽  
Reconstruction Accuracy ◽  
Strain Fields ◽  
Spatial Properties ◽  
Reconstruction Methods ◽  
Reconstruction Quality ◽  
Made In

Two methods for reconstructing intragranular strain fields are developed for scanning three-dimensional X-ray diffraction (3DXRD). The methods are compared with a third approach where voxels are reconstructed independently of their neighbours [Hayashi, Setoyama & Seno (2017). Mater. Sci. Forum, 905, 157–164]. The 3D strain field of a tin grain, located within a sample of approximately 70 grains, is analysed and compared across reconstruction methods. Implicit assumptions of sub-problem independence, made in the independent voxel reconstruction method, are demonstrated to introduce bias and reduce reconstruction accuracy. It is verified that the two proposed methods remedy these problems by taking the spatial properties of the inverse problem into account. Improvements in reconstruction quality achieved by the two proposed methods are further supported by reconstructions using synthetic diffraction data.

Shale Digital Core Image Generation Based on Generative Adversarial Networks

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Wenshu Zha ◽  
Xingbao Li ◽  
Daolun Li ◽  
Yan Xing ◽  
Lei He ◽  
...  
Keyword(s):  
Organic Matter ◽  
Morphological Characteristics ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Core Samples ◽  
Adversarial Networks ◽  
Training Samples ◽  
Digital Core ◽  
Artificial Intelligence Models ◽  
Convolution Properties

Abstract Stochastic reconstruction of digital core images is a vital part of digital core physics analysis, aiming to generate representative microstructure samples for sampling and uncertainty quantification analysis. This paper proposes a novel reconstruction method of the digital core of shale based on generative adversarial networks (GANs) with powerful capabilities of the generation of samples. GANs are a series of unsupervised generative artificial intelligence models that take the noise vector as an input. In this paper, the GANs with a generative and a discriminative network are created respectively, and the shale image with 45 nm/pixel preprocessed by the three-value-segmentation method is used as training samples. The generative network is used to learn the distribution of real training samples, and the discriminative network is used to distinguish real samples from synthetic ones. Finally, realistic digital core samples of shale are successfully reconstructed through the adversarial training process. We used the Fréchet inception distance (FID) and Kernel inception distance (KID) to evaluate the ability of GANs to generate real digital core samples of shale. The comparison of the morphological characteristics between them, such as the ratio of organic matter and specific surface area of organic matter, indicates that real and reconstructed samples are highly close. The results show that deep convolutional generative adversarial networks with full convolution properties can reconstruct digital core samples of shale effectively. Therefore, compared with the classical methods of reconstruction, the new reconstruction method is more promising.

Research on Types of Oil Reservoirs and Character of Oil-Water Distribution in Heidimiao Layer

2013 ◽  
Vol 772 ◽  
pp. 789-794
Author(s):  
Gui You Lv
Keyword(s):  
Water Distribution ◽  
Oil And Gas ◽  
Dominant Role ◽  
Three Dimensional ◽  
Physical Property ◽  
Research Area ◽  
Oil Reservoir ◽  
Well Testing ◽  
Testing Data ◽  
Oil Water

This paper takes Yingtai area which is located in the south of Qijia-Gulong sag and part of central sag area in the north of the Songliao Basin as the research area. Then combining all information of core, logging, three-dimensional seism and well testing data, it studies the reservoir type and oil-water distribution characteristics of Heidimiao by analyzing the comparison charts of sandstone, profile map of reservoir, T07 structure diagram, well testing data, stratum thickness, sandstone thickness, ratio of sandstone thickness to stratum thickness, porosity values, permeability contour maps. The reservoir lithology of Heidimiao oil layer is siltstone-oriented with poor physical property. The main controlling factor of oil-water distribution is the lithology, followed by the structure. Heidimiao oil layer mainly includes three types, lithological oil reservoir, lithological - structural oil reservoir and structural oil reservoir, among which lithological reservoir plays a dominant role. Its oil-water distribution is characterized by the pattern of upper-water and bottom-oil; when the fault acts as the pathway for the longitudinal migration of oil and gas, the pattern changes to the upper-oil and bottom-water. This research could provide reliable geological basis for the research of old well re-examination, favorable area evaluation and horizontal well drilling design.

scholarly journals Development of 3D Tissue Reconstruction Method from Single-cell RNA-seq Data

2017 ◽  
Vol 3 (1) ◽  
pp. 53 ◽  
Author(s):  
Tomoya Mori ◽  
Junko Yamane ◽  
Kenta Kobayashi ◽  
Nobuko Taniyama ◽  
Takanori Tano ◽  
...  
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Expression Profiles ◽  
Marker Gene ◽  
Three Dimensional ◽  
Principal Component ◽  
Marker Genes ◽  
Reconstruction Method ◽  
Reconstruction Methods ◽  
Tissue Reconstruction

In silico three-dimensional (3D) reconstruction of tissues/organs based on single-cell profiles is required to comprehensively understand how individual cells are organized in actual tissues/organs. Although several tissue reconstruction methods have been developed, they are still insufficient to map cells on the original tissues in terms of both scale and quality. In this study, we aim to develop a novel informatics approach which can reconstruct whole and various tissues/organs in silico. As the first step of this project, we conducted single-cell transcriptome analysis of 38 individual cells obtained from two mouse blastocysts (E3.5d) and tried to reconstruct blastocyst structures in 3D. In reconstruction step, each cell position is estimated by 3D principal component analysis and expression profiles of cell adhesion genes as well as other marker genes. In addition, we also proposed a reconstruction method without using marker gene information. The resulting reconstructed blastocyst structures implied an indirect relationship between the genes of Myh9 and Oct4.

Novel microscopic imager instrument for rock and fluid imaging

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. E251-E262 ◽  
Author(s):  
Marc H. Schneider ◽  
Patrick Tabeling ◽  
Fadhel Rezgui ◽  
Martin G. Lüling ◽  
Aurelien Daynes
Keyword(s):  
Oil And Gas ◽  
Image Interpretation ◽  
Core Sample ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Oil And Gas Exploration ◽  
The Core ◽  
Video Acquisition ◽  
Visible Light Range ◽  
Acquisition Mode

Core analysis from reservoir rock plays an important role in oil and gas exploration as it can provide a large number of rock properties. Some of these rock properties can be extracted by image analysis of microscopic rock images in the visible light range. Such properties include the size, shape, and distribution of pores and grains, or more generally the texture, mineral distribution, and so on. A novel laboratory instrument and method allows for easy and reliable core imaging. This method is applicable even when the core sample is in poor shape. The capabilities of this technique can be verified by core images, image interpretation, and dynamic measurements of rock samples during flooding. A microscopic imager instrument is operated in video acquisition mode and can measure additional properties, such as fluid mobility, by detecting the emergence of injected fluids across the core sample.

Improvement of generalization capability of two-dimensional salt segmentation via iterative semi-supervised learning

2022 ◽  
pp. 1-43
Author(s):  
Lingxiao Jia ◽  
Satyakee Sen ◽  
Subhashis Mallick
Keyword(s):  
Oil And Gas ◽  
Three Dimensional ◽  
Ground Truth ◽  
Unlabeled Data ◽  
New Method ◽  
Two Dimensional ◽  
Learning Tools ◽  
Generalization Capability ◽  
Oil And Gas Exploration ◽  
Depth Images

Accurate interpretations of subsurface salts are vital to oil and gas exploration. Manually interpreting them from seismic depth images, however, is labor-intensive. Consequently, use of deep learning tools such as a convolutional neural network for automatic salt interpretation recently became popular. Because of poor generalization capabilities, interpreting salt boundaries using these tools is difficult when labeled data are available from one geological region and we like to make predictions for other nearby regions with varied geological features. At the same time, due to vast amount of the data involved and the associated computational complexities needed for training, such generalization is necessary for solving practical salt interpretation problems. In this work, we propose a semi-supervised training, which allows the predicted model to iteratively improve as more and more information is distilled from the unlabeled data into the model. In addition, by performing mixup between labeled and unlabeled data during training, we encourage the predicted models to linearly behave across training samples; thereby improving the generalization capability of the method. For each iteration, we use the model obtained from previous iteration to generate pseudo labels for the unlabeled data. This automated consecutive data distillation allows our model prediction to improve with iteration, without any need for human intervention. To demonstrate the effectiveness and efficiency, we apply the method on two-dimensional images extracted from a real three-dimensional seismic data volume. By comparing our predictions and fully supervised baseline predictions with those that were manually interpreted and we consider as “ground truth”, we find than the prediction quality our new method surpasses the baseline prediction. We therefore conclude that our new method is a viable tool for automated salt delineation from seismic depth images.

A Study on the Evolution of Distributary Channels in River-Dominated Deltas Using Numerical Simulations of Hydrodynamic Behaviors

2015 ◽  
Vol 744-746 ◽  
pp. 1050-1055
Author(s):  
Yang Jun Wang ◽  
Tai Ju Yin ◽  
Zhi Hao Deng
Keyword(s):  
Numerical Simulation ◽  
Numerical Model ◽  
Quantitative Methods ◽  
Oil And Gas ◽  
Reference Model ◽  
Three Dimensional ◽  
Hydrodynamic Characteristics ◽  
Oil And Gas Exploration ◽  
Hydrodynamic Behaviors ◽  
Distributary Channel

The Fluvial-dominated delta is one of the extremely important deposition systems in oil and gas exploration. In this paper, the three-dimensional numerical simulation of hydrodynamics has been applied to the precise analysis of the formation of fluvial-dominated deltas and the evolution of their distributary channels. The model has been created using the Delft3D program, and the conditions of the numerical model have been set according to the hydrodynamic characteristics of modern rivers and deltas. The calculation field was 20.5 km in length by 10 km in width. With the Mor-Factor set to 60, the simulation time was 45 days. The formation and the avulsion of the mouth bar, as well as the extension, migration and bifurcation of distributary channels, have been observed and studied through analysis of the simulation results. The vertical cross-section shows that the distributary channel was filled multiple times. According to distributary channel evolution characteristics combined with quantitative methods, the terminal distributary channels can be extremely developed under ideal conditions. Due to the cross-cutting and reform effort of distributary channels, sediments were spread widely and continuously. The results show that the numerical model works well in explaining the process of evolution in fluvial-dominated delta distributary channels. This study not only enables us to quantitatively understand the dynamic processes of terminal distributary channels in fluvial-dominated delta systems, but also provides a reference model for numerical simulation of hydrodynamics in sedimentology study.

scholarly journals Attention-Based 3D Human Pose Sequence Refinement Network

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4572
Author(s):  
Do-Yeop Kim ◽  
Ju-Yong Chang
Keyword(s):  
Three Dimensional ◽  
Video Data ◽  
Weighted Averaging ◽  
Reconstruction Method ◽  
Unit Quaternion ◽  
Reconstruction Methods ◽  
In The Wild ◽  
Human Pose ◽  
Mesh Reconstruction ◽  
Weight Regression

Three-dimensional human mesh reconstruction from a single video has made much progress in recent years due to the advances in deep learning. However, previous methods still often reconstruct temporally noisy pose and mesh sequences given in-the-wild video data. To address this problem, we propose a human pose refinement network (HPR-Net) based on a non-local attention mechanism. The pipeline of the proposed framework consists of a weight-regression module, a weighted-averaging module, and a skinned multi-person linear (SMPL) module. First, the weight-regression module creates pose affinity weights from a 3D human pose sequence represented in a unit quaternion form. Next, the weighted-averaging module generates a refined 3D pose sequence by performing temporal weighted averaging using the generated affinity weights. Finally, the refined pose sequence is converted into a human mesh sequence using the SMPL module. HPR-Net is a simple but effective post-processing network that can substantially improve the accuracy and temporal smoothness of 3D human mesh sequences obtained from an input video by existing human mesh reconstruction methods. Our experiments show that the noisy results of the existing methods are consistently improved using the proposed method on various real datasets. Notably, our proposed method reduces the pose and acceleration errors of VIBE, the existing state-of-the-art human mesh reconstruction method, by 1.4% and 66.5%, respectively, on the 3DPW dataset.

scholarly journals Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

2019 ◽  
Vol 9 (3) ◽  
pp. 591
Author(s):  
Wei-Chao Shi ◽  
Jian-Ming Zheng ◽  
Yan Li ◽  
Xu-Bo Li
Keyword(s):  
Surface Topography ◽  
Three Dimensional ◽  
Surface Characteristics ◽  
Reconstruction Algorithm ◽  
Shape From Shading ◽  
Reconstruction Method ◽  
Machined Surface ◽  
Reconstruction Methods ◽  
Product Function ◽  
Dimensional Reconstruction

In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.

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