Shale lamina thickness study based on micro-scale image processing of thin sections

Abstract Use Image-Pro Plus software, high-precision inductive sensor TR200, electro n microscope and ENVI image processing system to quantitatively study aggregate mo rphology from three scales: macro, meso, and micro, and obtain P, As, R, Ry, Rsm, et c. 17 quantitative indicators of morphological characteristics, compared with the occup ancy rate of the aggregate broken surface and the roughness of the aggregate surface. T he results show that the 17 morphological characteristics indexes are feasible for multi -scale quantification of aggregate morphology. Macro-morphological characteristics di rectly indicate the angularity and needle-like shape of materials, micro-morphological characteristics directly indicate the roughness of materials surface, and micro-morphol ogical characteristics directly indicate the micro-morphological characteristics of mate rials. The quantitative indexes of aggregate morphology at micro-scale and micro-scal e are improved, and the results are helpful to reveal the morphological characteristics o f aggregate comprehensively.

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Automatic identification of minerals in thin sections using image processing

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03474-5 ◽

2021 ◽

Author(s):

Amineh Naseri ◽

Ali Rezaei Nasab

Keyword(s):

Image Processing ◽

Automatic Identification ◽

Thin Sections

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Geometric and Fractal Characterization of Pore Systems in the Upper Triassic Dolomites Based on Image Processing Techniques (Example from Žumberak Mts, NW Croatia)

Sustainability ◽

10.3390/su13147668 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7668

Author(s):

Ivica Pavičić ◽

Zlatko Briševac ◽

Anja Vrbaški ◽

Tonći Grgasović ◽

Željko Duić ◽

...

Keyword(s):

Image Processing ◽

Effective Porosity ◽

Upper Triassic ◽

Particle Analysis ◽

Thin Sections ◽

Carpathian Region ◽

Groundwater Aquifers ◽

Distribution Parameters ◽

Processing Techniques

Karst aquifers are important sources of thermal and groundwater in many parts of the world, such as the Alpine–Dinaric–Carpathian region in Europe. The Upper Triassic dolomites are regionally recognized thermal and groundwater aquifers but also hydrocarbon reservoirs. They are characterized by predominantly fractured porosity, but the actual share of depositional and diagenetic porosity is rarely investigated. In this research, we presented the geometric characterization of the measured microporosity of the Upper Triassic dolomites of the Žumberak Mts (Croatia), through thin-section image processing and particle analysis techniques. Pore parameters were analyzed on microphotographs of impregnated thin sections in scale. A total of 2267 pores were isolated and analyzed. The following parameters were analyzed: pore area, pore perimeter, circularity, aspect ratio (AR), roundness, solidity, Feret AR, compactness, and fractal dimension. Furthermore, porosity was calculated based on the pore portion in each image. The effective porosity on rock samples was determined using saturation and buoyancy techniques as an accompanying research method. We analyzed distributions of each parameter, their correlation, and most of the parameters are characterized by an asymmetric or asymmetric normal distribution. Parameters that quantify pore irregularities have similar distributions, and their values indicate the high complexity of the pore geometry, which can significantly impact permeability.

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Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

10.36227/techrxiv.16961551.v1 ◽

2021 ◽

Author(s):

Omar Alfarisi ◽

Aikifa Raza ◽

Hongtao Zhang ◽

Mohamed Sassi ◽

TieJun Zhang

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Image Recognition ◽

Reference Values ◽

Carbonate Rock ◽

Image Resolution ◽

Blind Test ◽

Thin Sections ◽

Clastic Rocks

Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.

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Comparison Between the Classical Classification and Digital Classification for Selected Samples of Igneous and Carbonate Rocks

Iraqi Geological Journal ◽

10.46717/igj.54.1c.2ms-2021-03-22 ◽

2021 ◽

Vol 54 (1C) ◽

pp. 16-29

Author(s):

Shareef Al-Hamed

Keyword(s):

Image Processing ◽

Digital Image Processing ◽

Digital Image ◽

Carbonate Rocks ◽

Volcanic Rocks ◽

Igneous Rocks ◽

Thin Sections ◽

Major Oxides ◽

Reliable Classification ◽

Mineralogical Compositions

As igneous rocks have widely chemical and mineralogical compositions, there are many ways to classify these rocks. These ways are classical approved methods to give a reliable classification and nomenclature of rocks. Some igneous rocks may be classified by digital image processing to assist in classical methods. Five igneous samples were cut, prepared of thin sections, and polished to classify them by classical methods and digital image processing by ENVI software. Moreover, part of these samples crushed an analysis of major oxides. The current igneous samples have referred to the basic and mesocratic rocks based on the classical methods and this has corresponded to ENVI software. The igneous samples have reflected the leucogabbros when classify them by classical and ENVI classifications, except the G5 sample, which has been referred to as gabbro by ENVI. There is a clear similarity between the classical and ENVI classifications. ENVI classification is a reliable classification to assist the classical methods in the nomenclature of igneous rocks, especially, plutonic rocks, it can be also applied to thin sections of volcanic rocks to classify and nomenclature classification by ENVI has been applied on fifty thin sections of limestones to identify microfacies which are classified beforehand by classical (optical) classification. According to optical classification, microfacies have classified as mudstone, wackestone, packstone, and grainstone. When the digital classification is applied to them, there is no grainstone texture found in these them. Digital thin sections, where the true name of these microfacies is packstone. Therefore, the positive sides of the digital image processing by ENVI software appeared and contrasted to the optical classification which contained some mistakes when applied to the nomenclature of these microfacies.

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Fully autonomous mini/micro scale UAV field experiences and image processing applications

2018 26th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2018.8404349 ◽

2018 ◽

Cited By ~ 2

Author(s):

Dilek Basaran Bil ◽

Erhan Ilhan Konukseven

Keyword(s):

Image Processing ◽

Field Experiences ◽

Micro Scale

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Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

10.36227/techrxiv.16961551 ◽

2021 ◽

Author(s):

Omar Alfarisi ◽

Aikifa Raza ◽

Hongtao Zhang ◽

Mohamed Sassi ◽

TieJun Zhang

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Image Recognition ◽

Reference Values ◽

Carbonate Rock ◽

Image Resolution ◽

Blind Test ◽

Thin Sections ◽

Clastic Rocks

Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.

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