Image analysis of plagioclase crystals in rock thin sections using grey level homogeneity recognition of discrete areas

2007 ◽  
Vol 33 (3) ◽  
pp. 335-356 ◽  
Author(s):  
John V. Smith ◽  
Eberhard Beermann
Keyword(s):  
Image Analysis ◽  
Grey Level ◽  
Thin Sections

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

QUANTIFICATION OF POROSITY IN SUBSTRATES USING THIN SECTIONS AND IMAGE ANALYSIS

2012 ◽  
pp. 105-110
Author(s):  
L. Pineda-Marín ◽  
M.C. Gutiérrez-Castorena ◽  
R. Anicua-Sánchez ◽  
L. Cajuste-Bontemps ◽  
E.V. Gutiérrez-Castorena
Keyword(s):  
Image Analysis ◽  
Thin Sections

scholarly journals An Overview of the Topological Gradient Approach in Image Processing: Advantages and Inconveniences

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Lamia Jaafar Belaid
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Numerical Experiments ◽  
Grey Level ◽  
Major Drawback ◽  
Topological Gradient ◽  
Boundary Measurements ◽  
Topological Asymptotic Expansion ◽  
Gradient Approach ◽  
Segmentation Problem

Image analysis by topological gradient approach is a technique based upon the historic application of the topological asymptotic expansion to crack localization problem from boundary measurements. This paper aims at reviewing this methodology through various applications in image processing; in particular image restoration with edge detection, classification and segmentation problems for both grey level and color images is presented in this work. The numerical experiments show the efficiency of the topological gradient approach for modelling and solving different image analysis problems. However, the topological gradient approach presents a major drawback: the identified edges are not connected and then the results obtained particularly for the segmentation problem can be degraded. To overcome this inconvenience, we propose an alternative solution by combining the topological gradient approach with the watershed technique. The numerical results obtained using the coupled method are very interesting.

Pilot study on laser propagation in maxillary and mandibular bone: Grey level image analysis for optical measurements

2017 ◽  
Vol 18 ◽  
pp. 226-231 ◽  
Author(s):  
Monalisa Jacob Guiselini ◽  
Alessandro Melo Deana ◽  
Daniela de Fátima Teixeira da Silva ◽  
Nelson Hideyoshi Koshoji ◽  
Raquel Agnelli Mesquita-Ferrari ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pilot Study ◽  
Optical Measurements ◽  
Grey Level ◽  
Mandibular Bone ◽  
Laser Propagation ◽  
Grey Level Image

Development of a novel image analysis procedure to quantify biological porosity and illuvial clay in large soil thin sections

Geoderma ◽  
2017 ◽  
Vol 292 ◽  
pp. 135-148 ◽  
Author(s):  
Ophélie Sauzet ◽  
Cécilia Cammas ◽  
Jean Marc Gilliot ◽  
Manon Bajard ◽  
David Montagne
Keyword(s):  
Image Analysis ◽  
Analysis Procedure ◽  
Thin Sections ◽  
Large Soil

An automated procedure for computing the packing properties of dense and locked sands by image analysis of thin sections

2014 ◽  
Vol 16 (6) ◽  
pp. 867-880 ◽  
Author(s):  
Clara Celauro ◽  
Maurizio Ziccarelli ◽  
Giuseppe Parla ◽  
Calogero Valore
Keyword(s):  
Image Analysis ◽  
Thin Sections

scholarly journals Porosity and Permeability Measurements Integration of The Upper Cretaceous in Balad Field, Central Iraq

2021 ◽  
Vol 54 (1B) ◽  
pp. 24-42
Author(s):  
Fawzi Al-Beyati
Keyword(s):  
Image Analysis ◽  
Log Analysis ◽  
Core Analysis ◽  
Well Log ◽  
Reservoir Properties ◽  
Thin Sections ◽  
Core Samples ◽  
Porosity And Permeability ◽  
Well Log Analysis ◽  
Porosity Range

The corrected porosity image analysis and log data can be used to build 3D models for porosity and permeability. This can be much realistic porosity obtainable because the core test data is not always available due to high cost which is a challenge for petroleum companies and petrophysists. Thus, this method can be used as an advantage of thin section studies and for opening horizon for more studies in the future to obtain reservoir properties. Seventy-two core samples were selected and the same numbers of thin sections were made from Khasib, Sa`di, and Hartha, formations from Ba-1, Ba-4, and Ba-8 wells, Balad Oilfield in Central Iraq to make a comprehensive view of using porosity image analysis software to determine the porosity. The petrophysical description including porosity image analysis was utilized and both laboratory core test analysis and well log analysis were used to correct and calibrate the results. The main reservoir properties including porosity and permeability were measured based on core samples laboratory analysis. The results of porosity obtained from well log analysis and porosity image analysis method are corrected by using SPSS software; the results revealed good correlation coefficients between 0.684 and 0.872. The porosity range values are 9-16% and 9-27% for Khasib and Sa’di in Ba-1 Well, respectively; 10-21%, 9-25%, and 16-27% for Khasib, Sa’di and Hartha in Ba-4 Well, respectively; and 11-24% and 15-24% for Khasib and Hartha in Ba-8 Well, respectively according to petrographic image analysis. By using the laboratory core analysis, the porosity range values are 12-26% and 17-24% for Khasib and Sa’di in Ba-1 Well, respectively; 6-28% and 14-27% for Sa’di and Hartha in Ba-4 Well, respectively; and 17-19% and 15-24% for Sa’di and Hartha in Ba-8 Well, respectively. Finally, the well log analysis showed that the porosity range values are 11-16% and 7-27% for Khasib and Sa’di in Ba-1 Well, respectively; 4-18%, 21-26%, and 16-19% for Khasib, Sa’di and Hartha in Ba-4 Well, respectively; and 9-24% and 15-23% for Khasib and Hartha in Ba-8 Well, respectively. The permeability range values based on laboratory core analysis are 1.51-8.97 md and 0.29-2.77 md for Khasib and Sa’di in Ba-1 Well, respectively; 0.01-24.5 md and 0.28-6.47 md for Sa’di and Hartha in Ba-4 Well, respectively; and 0.86-2.25 md and 0.23-3.66 for Sa’di and Hartha in Ba-8 Well, respectively.

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