[HTML][HTML] Fluid–rock interactions and its implications on EOR: Critical analysis, experimental techniques and knowledge gaps
Abstract Characterization of fluid–rock interactions is essential for a broad range of
subsurface applications such as understanding fluid flow in porous medium and enhanced …
subsurface applications such as understanding fluid flow in porous medium and enhanced …
Review on Pore Structure Characterization and Microscopic Flow Mechanism of CO2 Flooding in Porous Media
Y Tang, C Hou, Y He, Y Wang, Y Chen… - Energy …, 2021 - Wiley Online Library
Understanding of pore structure and microscopic flow mechanism at pore‐scale is
significant for enhancing oil recovery by carbon dioxide (CO2) flooding. Herein, the pore …
significant for enhancing oil recovery by carbon dioxide (CO2) flooding. Herein, the pore …
System for automated geoscientific analyses (SAGA) v. 2.1. 4
O Conrad, B Bechtel, M Bock, H Dietrich… - Geoscientific model …, 2015 - gmd.copernicus.org
The System for Automated Geoscientific Analyses (SAGA) is an open source geographic
information system (GIS), mainly licensed under the GNU General Public License. Since its …
information system (GIS), mainly licensed under the GNU General Public License. Since its …
Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking
Scanning electron microscope (SEM) image analysis facilitates the visualization and
quantification of the microstructure, topology, morphology, and connectivity of distinct …
quantification of the microstructure, topology, morphology, and connectivity of distinct …
Fully automated carbonate petrography using deep convolutional neural networks
Carbonate rocks are important archives of past ocean conditions as well as hosts of
economic resources such as hydrocarbons, water, and minerals. Geologists typically …
economic resources such as hydrocarbons, water, and minerals. Geologists typically …
Rock classification in petrographic thin section images based on concatenated convolutional neural networks
C Su, S Xu, K Zhu, X Zhang - Earth Science Informatics, 2020 - Springer
Rock classification plays an important role in rock mechanics, petrology, mining
engineering, magmatic processes, and numerous other fields pertaining to geosciences …
engineering, magmatic processes, and numerous other fields pertaining to geosciences …
Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy
EJY Koh, E Amini, GJ McLachlan, N Beaton - Minerals Engineering, 2021 - Elsevier
Thin section microscopy has been historically used for modal mineralogy in exploration and
for monitoring plant performance. Despite this, the technique relies on visual detection from …
for monitoring plant performance. Despite this, the technique relies on visual detection from …
Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales
For purposes of locating kerogen/organic matter and pores in SEM images of shale
samples, we tested an automated SEM-image segmentation workflow involving feature …
samples, we tested an automated SEM-image segmentation workflow involving feature …
Classification of igneous rocks from petrographic thin section images using convolutional neural network
Rock classification from petrographic thin section analysis often requires expertise in
mineralogy. This study developed a deep learning approach based on a convolutional …
mineralogy. This study developed a deep learning approach based on a convolutional …
Image processing and machine learning approaches for petrographic thin section analysis
S Budennyy, A Pachezhertsev, A Bukharev… - SPE Russian …, 2017 - onepetro.org
The article presents the methodology of petrographic thin section analysis, combining the
algorithms of image processing and statistical learning. The methodology includes the …
algorithms of image processing and statistical learning. The methodology includes the …