Petrophysical properties for CSG reservoirs from 3D imaging at multiple scales

2012 ◽  
Vol 52 (2) ◽  
pp. 694
Author(s):  
Alexandra Golab ◽  
Mark Knackstedt ◽  
Thomas McKay ◽  
C Ward ◽  
Val Pinczewski
Keyword(s):  
Multiple Scales ◽  
Flow Characteristics ◽  
Image Data ◽  
Large Field ◽  
Fracture Aperture ◽  
Petrophysical Properties ◽  
Fracture Permeability ◽  
X Ray ◽  
Reservoir Evaluation ◽  
Using Data

CSCSG reservoirs are intrinsically heterogeneous on every scale and the permeability and producibility of CSG is decreased when the pores and fractures are filled with minerals. The 3D characterisation and quantification of pore connectivity, cleat/fracture aperture and spacing, and extent of mineral infilling in coal is required for CSG reservoir evaluation of gas storage and flow characteristics. A technique has been developed to determine petrophysical properties of coal using data from a large-field, 3D microfocus X-ray computed tomography (µCT) at multiple scales, combined with SEM imaging, and automated mineralogy by QEMSCAN. µCT is a non-destructive technique and the X-ray densities of coal components are distinct; therefore, the pore/fracture, mineral, and coal matrix can be differentiated and quantified in 3D. The high resolution 3D image data can then be used to measure petrophysical properties. Specifically, this technique characterises porosity and its connectivity, cleat/fracture networks (aperture and spacing), cleat/fracture permeability, and mineral occurrences in 3D to better describe CSG reservoirs. The technique has been tested on samples of bituminous coal from a number of coalfields in the Sydney and Bowen Basins, Australia. The samples imaged were from 110–114 mm in diameter, yielding voxels ranging from 54–63 µm in size. The results can determine the depositional and post-depositional history of coal seams, in coal preparation and use, and in seam gas studies.

scholarly journals Characterization of flow units, rock and pore types for Mishrif Reservoir in West Qurna oilfield, Southern Iraq by using lithofacies data

2021 ◽  
Vol 11 (11) ◽  
pp. 4005-4018
Author(s):  
Ahmed N. Al-Dujaili ◽  
Mehdi Shabani ◽  
Mohammed S. AL-Jawad
Keyword(s):  
Flow Characteristics ◽  
Petrophysical Properties ◽  
Pore Throat ◽  
Throat Radius ◽  
Pore Characteristics ◽  
Storage Mechanism ◽  
Reservoir Evaluation ◽  
Flow Units ◽  
Wide Range ◽  
Southern Iraq

AbstractThis study has been accomplished by testing three different models to determine rocks type, pore throat radius, and flow units for Mishrif Formation in West Qurna oilfield in Southern Iraq based on Mishrif full diameter cores from 20 wells. The three models that were used in this study were Lucia rocks type classification, Winland plot was utilized to determine the pore throat radius depending on the mercury injection test (r35), and (FZI) concepts to identify flow units which enabled us to recognize the differences between Mishrif units in these three categories. The study of pore characteristics is very significant in reservoir evaluation. It controls the storage mechanism and reservoir fluid properties of the permeable units while pore structure is a critical controlling factor for the petrophysical properties and multiphase-flow characteristics in reservoir rocks. Flow zone indicator (FZI) has been used to identify the hydraulic flow units approach (HFUs). Each (HFU) was reproduced by certain FZI and was supposed to have similar geological and petrophysical properties. The samples were from four lithofacies, mA, CRII, mB1, and mB2. Because of the wide range of cored-wells samples (20 wells), this paper is updated the previous studies and indicated some differences in the resulting categories. It was noticed as results of this study that the rocks types of the lower Mishrif were mostly ranged from wackestone to packstone in the upper part of mB2 which reflected mid-ramp facies while the upper part of mB2 referred to shoal facies and for the mB1 unit the rocks types mostly range from packstone to grainstone with some points as wackestone marked as shoal and rudist bioherm facies. Grainstone relatively decreases with the increasing of depth from upper to lower Mishrif while wackestone and packstone indicated increasing in the same direction. The unit mA is marked as mesopores and macropores, while megapores and macropores feature increased in mB1 which has been noticed in the northern part of West Qurna oilfield due to increasing shoal and rudist bioherm facies, the mB2 unit revealed increasing in mesoporous and decreasing in megaporous. The upper Mishrif (mA) has three flow units, while the lower Mishrif (mB1, mB2) has eight flow units four for each reservoir unit.

Numerical analysis of shift error in X-ray phase contrast imaging for large field of view

2017 ◽  
Vol 34 (1) ◽  
pp. 8
Author(s):  
Jianheng Huang ◽  
Yaohu Lei ◽  
Xin Liu ◽  
Jinchuan Guo ◽  
Ji Li ◽  
...  
Keyword(s):  
Numerical Analysis ◽  
Phase Contrast ◽  
Field Of View ◽  
Phase Contrast Imaging ◽  
Large Field ◽  
Contrast Imaging ◽  
X Ray

scholarly journals UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2650
Author(s):  
Daegyun Choi ◽  
William Bell ◽  
Donghoon Kim ◽  
Jichul Kim
Keyword(s):  
Visual Information ◽  
Crack Detection ◽  
Image Data ◽  
Image Processing Techniques ◽  
Location Determination ◽  
Aerial Vehicle ◽  
Using Data ◽  
Processing Techniques ◽  
Deep Learning Model ◽  
Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

Hairline breakage detection in X-ray images using data fusion

2017 ◽  
Vol 77 (13) ◽  
pp. 17207-17222 ◽  
Author(s):  
C. Harriet Linda ◽  
G. Wiselin Jiji
Keyword(s):  
Data Fusion ◽  
X Ray ◽  
Using Data

Chest Imaging with Dual-Energy Subtraction Digital Tomosynthesis

1993 ◽  
Vol 34 (4) ◽  
pp. 346-350 ◽  
Author(s):  
S. Sone ◽  
T. Kasuga ◽  
F. Sakai ◽  
H. Hirano ◽  
K. Kubo ◽  
...  
Keyword(s):  
Low Voltage ◽  
Image Data ◽  
Dual Energy ◽  
Processing Unit ◽  
Digital Tomosynthesis ◽  
Chest Imaging ◽  
Layer Height ◽  
Pulmonary Lesions ◽  
X Ray ◽  
Energy Subtraction

Dual-energy subtraction digital tomosynthesis with pulsed X-ray and rapid kV switching was used to examine calcifications in pulmonary lesions. The digital tomosynthesis system used included a conventional fluororadiographic TV unit with linear tomographic capabilities, a high resolution videocamera, and an image processing unit. Low-voltage, high-voltage, and soft tissue subtracted or bone subtracted tomograms of any desired layer height were reconstructed from the image data acquired during a single tomographic swing. Calcifications, as well as their characteristics and distribution in pulmonary lesions, were clearly shown. The images also permitted discrimination of calcifications from dense fibrotic lesions. This technique was effective in demonstrating calcifications together with a solitary mass or disseminated nodules.

The Best of Both Worlds: 3D X-ray Microscopy with Ultra-high Resolution and a Large Field of View

2011 ◽  
Author(s):  
W. Li ◽  
J. Gelb ◽  
Y. Yang ◽  
Y. Guan ◽  
W. Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Field Of View ◽  
Large Field ◽  
X Ray

Flow Characteristics of Closed Loop High PRF E-Beam or X-ray-Assisted Discharge Exciplex Laser

1984 ◽  
pp. 375-382
Author(s):  
S. M. Fournier ◽  
M. L. Sentis ◽  
B. M. Forestier ◽  
B. L. Fontaine
Keyword(s):  
Closed Loop ◽  
Flow Characteristics ◽  
X Ray

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

scholarly journals Fullerite C60 optical constants in the C 1s NEXAFS region

2021 ◽  
Vol 2103 (1) ◽  
pp. 012168
Author(s):  
D V Sivkov ◽  
S V Nekipelov ◽  
O V Petrova ◽  
D V Bogachuk ◽  
R N Skandakov ◽  
...  
Keyword(s):  
Cross Sections ◽  
Optical Constants ◽  
Absorption Cross ◽  
X Ray ◽  
Carbon Absorption ◽  
Cross Section Spectrum ◽  
Integral Relations ◽  
Atomic Form Factor ◽  
Using Data ◽  
X Ray Absorption

Abstract Using data on the absorption cross sections the refraction coefficient spectral dependence n(E) and the spectra of the remaining optical coefficients (reflection coefficient, phase shift, and atomic form factor) in the fullerite C60 C 1s near edge X-ray absorption fine structure (NEXAFS) region (280–350 eV) were determined. For the n(E) calculations the Kramers-Kronig integral relations (KKRs) were used. The KKR computations were performed using data on atomic carbon absorption cross sections in the 10–30000 eV range and on solid and gaseous C60 – in the 0–120 eV. Absorption cross section spectrum in the fullerite C60 C 1s NEXAFS region were measured.

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