Digital rock segmentation for petrophysical analysis with reduced user bias using convolutional neural networks
Pore‐scale digital images are usually obtained from microcomputed tomography data that
has been segmented into void and grain space. Image segmentation is a crucial step in the …
has been segmented into void and grain space. Image segmentation is a crucial step in the …
Deep learning convolutional neural networks to predict porous media properties
N Alqahtani, RT Armstrong… - SPE Asia Pacific oil and …, 2018 - onepetro.org
Digital rocks obtained from high-resolution micro-computed tomography (micro-CT) imaging
has quickly emerged as a powerful tool for studying pore-scale transport phenomena in …
has quickly emerged as a powerful tool for studying pore-scale transport phenomena in …
Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images
Segmentation of 3D micro-Computed Tomographic (μ CT) images of rock samples is
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
Deep learning in pore scale imaging and modeling
Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …
Learning into the workflow, from image processing to simulating physical processes. In …
Application of deep learning for semantic segmentation of sandstone thin sections
Sedimentary petrology is the basis for most mineral and textural identification in sandstones.
Automating mineralogical interpretation of an entire thin section image has many practical …
Automating mineralogical interpretation of an entire thin section image has many practical …
Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks
M Reinhardt, A Jacob, S Sadeghnejad… - Environmental Earth …, 2022 - Springer
Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows,
affecting the analysis of physical rock properties. Conventional segmentation techniques …
affecting the analysis of physical rock properties. Conventional segmentation techniques …
Automatic segmentation tool for 3D digital rocks by deep learning
Obtaining an accurate segmentation of images obtained by computed microtomography
(micro-CT) techniques is a non-trivial process due to the wide range of noise types and …
(micro-CT) techniques is a non-trivial process due to the wide range of noise types and …
Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images
RA Rubo, C de Carvalho Carneiro, MF Michelon… - Journal of Petroleum …, 2019 - Elsevier
Images represent a large and efficient source of geological information from oil exploration.
To better analyze them, well-known machine learning algorithms are used to extract …
To better analyze them, well-known machine learning algorithms are used to extract …
An application of deep neural networks for segmentation of microtomographic images of rock samples
I Varfolomeev, I Yakimchuk, I Safonov - Computers, 2019 - mdpi.com
Image segmentation is a crucial step of almost any Digital Rock workflow. In this paper, we
propose an approach for generation of a labelled dataset and investigate an application of …
propose an approach for generation of a labelled dataset and investigate an application of …
Flow-based characterization of digital rock images using deep learning
X-ray imaging of porous media has revolutionized the interpretation of various microscale
phenomena in subsurface systems. The volumetric images acquired from this technology …
phenomena in subsurface systems. The volumetric images acquired from this technology …