Petrophysical properties prediction from prestack seismic data using convolutional neural networks
We have built convolutional neural networks (CNNs) to obtain petrophysical properties in
the depth domain from prestack seismic data in the time domain. We compare two workflows …
the depth domain from prestack seismic data in the time domain. We compare two workflows …
Digital rock physics benchmarks—Part I: Imaging and segmentation
The key paradigm of digital rock physics (DRP)“image and compute” implies imaging and
digitizing the pore space and mineral matrix of natural rock and then numerically simulating …
digitizing the pore space and mineral matrix of natural rock and then numerically simulating …
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 …
Petrographic microfacies classification with deep convolutional neural networks
Petrographic analysis is based on the microscopic description and classification of rocks
and is a crucial technique for sedimentary and diagenetic studies. When compared to hand …
and is a crucial technique for sedimentary and diagenetic studies. When compared to hand …
Industrial applications of digital rock technology
This article provides an overview of the current state of digital rock technology, with
emphasis on industrial applications. We show how imaging and image analysis can be …
emphasis on industrial applications. We show how imaging and image analysis can be …
[HTML][HTML] Machine and deep learning for estimating the permeability of complex carbonate rock from X-ray micro-computed tomography
M Tembely, AM AlSumaiti, WS Alameri - Energy Reports, 2021 - Elsevier
Accurate estimation of permeability is critical for oil and gas reservoir development and
management, as it controls production rate. After assessing numerical techniques ranging …
management, as it controls production rate. After assessing numerical techniques ranging …
Assessing the utility of FIB-SEM images for shale digital rock physics
S Kelly, H El-Sobky, C Torres-Verdín… - Advances in water …, 2016 - Elsevier
Shales and other unconventional or low permeability (tight) reservoirs house vast quantities
of hydrocarbons, often demonstrate considerable water uptake, and are potential …
of hydrocarbons, often demonstrate considerable water uptake, and are potential …
Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN
In digital rock physics, the study of physical parameters and flow characteristics of reservoirs
requires a wealth of three-dimension digital rock samples. However, traditional physical …
requires a wealth of three-dimension digital rock samples. However, traditional physical …
Point-cloud deep learning of porous media for permeability prediction
We propose a novel deep learning framework for predicting the permeability of porous
media from their digital images. Unlike convolutional neural networks, instead of feeding the …
media from their digital images. Unlike convolutional neural networks, instead of feeding the …
A deep learning perspective on predicting permeability in porous media from network modeling to direct simulation
Predicting the petrophysical properties of rock samples using micro-CT images has gained
significant attention recently. However, an accurate and an efficient numerical tool is still …
significant attention recently. However, an accurate and an efficient numerical tool is still …