Seismic gyrotropy as a result of rock-microstructure dissymmetry

1998 ◽  
Vol 30 (3) ◽  
pp. 251-265 ◽  
Author(s):  
T.I. Chichinina ◽  
I.R. Obolentseva
Keyword(s):  
Rock Microstructure

Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

2021 ◽  
Author(s):  
Ana Gabriela Reyna Flores ◽  
Quentin Fisher ◽  
Piroska Lorinczi
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Area Under The Curve ◽  
Tight Gas ◽  
Classification Problems ◽  
Tight Sandstone ◽  
Permeability Prediction ◽  
Rock Microstructure

Abstract Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

Influences of rock microstructure on acid dissolution at a dolomite surface

Geothermics ◽  
2022 ◽  
Vol 100 ◽  
pp. 102324
Author(s):  
Peng Xu ◽  
Mao Sheng ◽  
Tianyi Lin ◽  
Qing Liu ◽  
Xiaoguang Wang ◽  
...  
Keyword(s):  
Acid Dissolution ◽  
Rock Microstructure

Cullet-Filled Concrete

2020 ◽  
Vol 992 ◽  
pp. 73-78
Author(s):  
V.N. Shishkanova ◽  
M.V. Ivanko ◽  
Andrey Yu. Kozlov
Keyword(s):  
Silica Fume ◽  
Large Particle ◽  
Concrete Strength ◽  
Highly Dispersed Silica ◽  
Concrete Mixtures ◽  
Highly Dispersed ◽  
Polymer Component ◽  
Rock Microstructure ◽  
Very High ◽  
Cullet Content

The paper considers how cullet of different particle-size distribution affects the concrete strength. Experiments have proven that large-particle cullet (1.25 cm or larger) could be used as an aggregate; the concrete strength will be on par with those of ordinary natural/crushed sand concrete. The paper proves the feasibility of injecting highly dispersed silica fume in combination with effective polycarboxylate-based superplasticizers in cullet-based concrete mixtures. Highly dispersed silica fume will positively affect the strength characteristics of concrete, as silica fume in cement rock reacts with Са (ОН)2, which is released upon the hydration of the clinker minerals С3S and С2S; the reaction produces very strong compounds. Concretes containing up to 30% silica fume in combination with a superplasticizer will feature very high early strength. Use of strong aggregates with a 30% cullet content can produce strong concretes; after steamed, a concrete containing silica fume and polycarboxylate-based superplasticizer will reach 90% of the graded strength. Cement-rock microstructure studies show that the polymer component of the STACHEMENT 2280 superplasticizer will gradually transcend from the glass grains to the cement rock. The interface between the polymer-coated glass grains and the cement rock is blurred and barely present. This strengthens the glass-rock adhesion and improves the concrete strength. This is why cullet is recommended for use in the production of curb stones.

Wave-velocity dispersion and rock microstructure

2019 ◽  
Vol 183 ◽  
pp. 106466 ◽  
Author(s):  
Wei Cheng ◽  
Jing Ba ◽  
Li-Yun Fu ◽  
Maxim Lebedev
Keyword(s):  
Wave Velocity ◽  
Velocity Dispersion ◽  
Rock Microstructure

scholarly journals Rock Mechanical Property Influenced by Inhomogeneity

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Yunliang Tan ◽  
Dongmei Huang ◽  
Ze Zhang
Keyword(s):  
Mechanical Property ◽  
Fractal Dimension ◽  
Rock Type ◽  
Fractal Dimensions ◽  
Rock Material ◽  
The Self ◽  
Self Similarity ◽  
Red Sandstone ◽  
Variation Patterns ◽  
Rock Microstructure

In order to identify the microstructure inhomogeneity influence on rock mechanical property, SEM scanning test and fractal dimension estimation were adopted. The investigations showed that the self-similarity of rock microstructure markedly changes with the scanned microscale. Different rocks behave in different fractal dimension variation patterns with the scanned magnification, so it is conditional to adopt fractal dimension to describe rock material. Grey diabase and black diabase have high suitability; red sandstone has low suitability. The suitability of fractal-dimension-describing method for rocks depends on both investigating scale and rock type. The homogeneities of grey diabase, black diabase, grey sandstone, and red sandstone are 7.8, 5.7, 4.4, and 3.4, separately; their average fractal dimensions of microstructure are 2.06, 2.03, 1.72, and 1.40 correspondingly, so the homogeneity is well consistent with fractal dimension. For rock material, the stronger brittleness is, the less profile fractal dimension is. In a sense, brittleness is an image of rock inhomogeneity in macroscale, while profile fractal dimension is an image of rock inhomogeneity in microscale. To combine the test of brittleness with the estimation of fractal dimension with condition will be an effective approach for understanding rock failure mechanism, patterns, and behaviours.

Small-angle neutron scattering investigation of the effect of a heat-treatment on the microstructure of porous basalt rocks

2006 ◽  
Vol 70 (6) ◽  
pp. 689-695 ◽  
Author(s):  
A. Kahle ◽  
B. Winkler ◽  
A. Radulescu
Keyword(s):  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Fractal Structure ◽  
Pore Space ◽  
Internal Surface ◽  
Effect Of Temperature ◽  
Surface Areas ◽  
Heat Treated ◽  
Rock Microstructure

AbstractSmall-angle neutron scattering has been used to study the microstructure of natural porous basalt rocks. The effect of temperature on the rock microstructure has been investigated on ‘as received’ and heat-treated basalts. The magnitudes of α, the power-law scattering exponent were between 3 and 4 for the majority of the rocks, indicating a surface fractal structure between the basalt matrix and the pore space. Heat-treated basalts show higher α values, and therefore a smoother pore surface. Internal surface areas were determined for all basalts depending on the thermal history.

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