Estimating strength of sandstone using petrographic thin-section data
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Traditional petrographic number (PN) ratings of concrete aggregates cannot work reliably in Florida because most of the state’s rocks are too fine-grained for use of macroscopic petrographic evaluations. This study designed a thin-section approach to PN calculations, based on relevant carbonate rock components that can be seen easily in thin section. The primary lithologic features chosen to differentiate the carbonates are the types of allochems, the porosity type, the kind of cement or matrix, and any additional noncarbonate minerals. Ten sets of aggregate samples were examined (eight carbonate and two noncarbonate sets) to test the method and ranking of thin-section PN determination. Prior work with PN values showed that aggregates with PN values of less than 140 yield good field performance, 140 to 160 have fair to poor field performance, and those greater than 160 normally have poor performance. Results of the thin-section PN method found values for our samples ranging from 100 (an ideal aggregate) to 149. By using factor weights (FWs) developed, six aggregates have PNs below 140 (good), and four scored between 140 and 160 (fair to poor) and may need additional testing. This method of PN calculation is easy to learn, clearly focused on carbonate aggregates like those in Florida and surrounding regions, and relatively inexpansive, and with it, soundness predictions can be determined quickly (in approximately 1 h per sample). Correlation of thin-section PN values with field performance information has not yet been completed, however, and this must be done before predicted performance can be considered reliable.
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Rock classification plays an important role in rock mechanics, petrology, mining engineering, magmatic processes, and numerous other fields pertaining to geosciences. This study proposes a concatenated convolutional neural network (Con-CNN) method for classifying the geologic rock type based on petrographic thin sections. Herein, plane polarized light (PPL) and crossed polarized light (XPL) were used to acquire thin section images as the fundamental data. After conducting the necessary pre-processing analyses, the PPL and XPL images as well as their comprehensive image (CI) were incorporated in three convolutional neural networks (CNNs) comprising the same structure for achieving a preliminary classification; these images were developed by employing the fused principal component analysis (PCA). Subsequently, the results of the CNNs were concatenated by using the maximum likelihood detection to obtain a comprehensive classification result. Finally, a statistical revision was applied to fix the misclassification due to the proportion difference of minerals that were similar in appearance. In this study, 13 types of 92 rock samples, 196 petrographic thin sections, 588 images, and 63504 image patches were fabricated for the training and validation of the Con-CNN. The five-folds cross validation shows that the method proposed provides an overall accuracy of 89.97%, which facilitates the automation of rock classification in petrographic thin sections.
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Research Article| January 01, 1979 Estimating Strength of Sandstone Using Petrographic Thin-Section Data M. P. FAHY; M. P. FAHY M. P. Fahy is a geological engineer in the Resource Development and Technical Services Department of the Public Service Company of New Mexico, Albuquerque, New Mexico Search for other works by this author on: GSW Google Scholar M. J. GUCCIONE M. J. GUCCIONE M. J. Guccione is a Ph.D. candidate in the Geology Department of the University of Colorado and is presently completing her thesis work at the University of Arkansas Geology Department, Fayetteville, Arkansas. Search for other works by this author on: GSW Google Scholar Author and Article Information M. P. FAHY M. P. Fahy is a geological engineer in the Resource Development and Technical Services Department of the Public Service Company of New Mexico, Albuquerque, New Mexico M. J. GUCCIONE M. J. Guccione is a Ph.D. candidate in the Geology Department of the University of Colorado and is presently completing her thesis work at the University of Arkansas Geology Department, Fayetteville, Arkansas. Publisher: Association of Environmental & Engineering Geologists First Online: 02 Mar 2017 Online ISSN: 1558-9161 Print ISSN: 1078-7275 © 1979 Association of Engineering Geologists Environmental & Engineering Geoscience (1979) xvi (4): 467–485. https://doi.org/10.2113/gseegeosci.xvi.4.467 Article history First Online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation M. P. FAHY, M. J. GUCCIONE; Estimating Strength of Sandstone Using Petrographic Thin-Section Data. Environmental & Engineering Geoscience 1979;; xvi (4): 467–485. doi: https://doi.org/10.2113/gseegeosci.xvi.4.467 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Abstract The determination of rock compressive strength is a major design requirement for underground openings in rock. However, determination of compressive strength by triaxial compression tests requires high quality core samples which are not always available from thin-bedded, fractured strata. This investigation shows that the compressive strength of calcareous sandstones can be estimated from critical petrographic properties observed in thin sections. For the sandstones from the Beehive Coal Mine, the significant petrographic properties are mean and median grain sizes, percent quartz grains, total percent quartz, sphericity, and percent intergrown and percent straight grain contacts.The accuracy of estimating compressive strength can be improved by increasing the number of petrologic properties used as estimators. A polynomial prediction equation utilizing three easily measured rock properties, percent quartz grains, total percent cement, and mean grain size, shows very good correlation between estimated and experimental compressive strengths. This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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Abstract Sand as part of sedimentary material can be consisted of single mineral fragments, rock fragments, or even biogenic materials. Nusa Tenggara is an archipelago that was formed due to the meeting of the Indian Ocean Plate and the Eurasian Plate which supported by other geological processes. The stratigraphy of these islands is dominated by carbonate rocks, volcanic rocks, coastal deposits and alluvium. Due to the variation of the rock composition, there is a large possibility that mineralogy sediment material found in the coastal area will be different from one place to other. This study aims to analyse variations on coastal deposits in a number of specific locations in the Nusa Tenggara Islands. This study was carried out by mineralogical and petrographic analysis of sand samples from six coastal areas in Nusa Tenggara Islands. The mineral composition of coastal sand was identified using hand loupe to give general understanding. After that, the petrographic analyses were implemented to give detailed mineralogical analyses of each sand. Mineralogical studies are specialized in study of the physical structure and chemical composition of each mineral, while petrographic studies can support the details of every physical information obtained through macroscopic observations. Data are retrieved by taking sand samples directly in the research field in a certain amount. This sample is separated then for macroscopic analysis while the other parts are made in a thin section. The thin section will be used for petrographic analysis under a polarization microscope. The analysis result shows that there are variations in coastal sediment components and it most likely does to different rock found around such area.
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The alkali–silica reactivity potential of 20 different Quaternary natural aggregates (sands and gravels) from the Czech Republic has been evaluated by petrographic and dilatometric methods. Petrographic techniques, both qualitative (optical and scanning electron microscope (SEM)/energy dispersive spectrometer (EDS) microscopy of thin sections) and quantitative (petrographic image analysis of thin sections), have been applied for the disused specimens. The dilation of the mortar-bar test specimens (according to ASTM C1260) ranged nine samples to the non-reactive, nine samples to reactive and two samples to potentially reactive categories. These categories, based on the dilation values, do not however satisfactorily correlate to the volumes of individual groups of aggregates identified petrographically in mortar-bars. More detailed petrographic examination showed the subdivision according to the presence/absence of deformation sings inhibitors and/or grain size. These results show that the optical microscopy of mortar-bar specimens provides detailed identification of alkali reactive samples according to their spatial relationship with ASR products. The same approach also allows direct identification of gels and alkali–silica reactive fragments.
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This report presents the results of mineralogic and petrographic analyses performed on samples of salt-bearing rock from a potential repository site in the Palo Duro Basin, Texas. The samples are from Permian Units 4 and 5 salt, Lower San Andres Formation, J. Friemel No. 1 well, Deaf Smith County, Texas. The mineralogic and petrographic data were obtained from polished thin sections cut parallel to the axis of the core for each sample. The polished thin sections were examined in order to determine the abundances of soluble (halite) and insoluble components (anhydrite, clay, carbonate, quartz, gypsum, etc.). The information reported here includes mineral associations (detrital, authigenic, cement, alteration, etc.), texture, grain size, and sedimentary fabrics. The report also includes representative photomicrographs with superimposed bar scales. Photomicrographs of polished thin sections have the up-core direction designated. X-ray diffraction was also used for identification of soluble and insoluble minerals. 7 refs., 2 tabs.
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AbstractPetrophysical properties of petroleum reservoir rocks are usually obtained by laborious core laboratory measurements. The present study investigates the capability of petrographic image analysis applied on thin sections of reservoir rock and fuzzy logic for predicting porosity in carbonate rocks. The proposed methodology comprises two steps: first, the petrographic parameters, including porosity type, grain size, mean geometrical shape coefficient of grains, and texture type, were extracted for each thin section based on image analysis techniques. Consequently, the petrographic parameters were formulated to core porosity using a Takagi and Sugeno fuzzy inference system. Petrographic image analysis is an emerging technology, which provides fast and accurate quantitative evaluation from reservoir rock. The results of single petrographic image analysis showed inaccurate estimation of total porosity in all rocks except those that have an extremely isotropic pore structure. A quantitative evaluation of thin section images and fuzzy model was successfully used to improve the accuracy of porosity prediction and the results of thin section analysis were generalized to core plug analysis. The mean square error and correlation coefficient between two-dimensional measurements and core plug were obtained at 0.0262 and 86.3, respectively, which shows acceptable prediction of three-dimensional porosity from two-dimensional thin sections. Therefore, the results confirmed the validity of the propounded methodology.Keywords:: core laboratory measurementfuzzy logicpetrographic image analysisporosityreservoir rock ACKNOWLEDGMENTSThe authors would like to extend their appreciation to the Iranian Central Oil Fields Company for providing technical support during this research. They also are grateful to Dr. Amir Hatampour from the petrophysics department of Pars Oil and Gas Company (POGC) for helping during the research.FIGURE 3 (a) Crossplot showing correlation coefficients between fuzzy predicted porosity and measured core porosity. (b) A comparison between measured and fuzzy predicted porosity versus depth in test well.Display full size
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