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 …
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 …
Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks
Various rocks such as carbonate, coal or shale contain both micro-and macro-pores. To
accurately predict the fluid flow and mechanical properties of these porous media, a multi …
accurately predict the fluid flow and mechanical properties of these porous media, a multi …
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 …
Multimodal imaging and machine learning to enhance microscope images of shale
A machine learning based image processing workflow is presented to enhance shale
source rock microscopic images obtained using diverse imaging platforms. Images were …
source rock microscopic images obtained using diverse imaging platforms. Images were …
Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm
Accounting for the morphology of shale formations, which represent highly heterogeneous
porous media, is a difficult problem. Although two-or three-dimensional images of such …
porous media, is a difficult problem. Although two-or three-dimensional images of such …
PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media
Abstract We present the PoreFlow-Net, a 3D convolutional neural network architecture that
provides fast and accurate fluid flow predictions for 3D digital rock images. We trained our …
provides fast and accurate fluid flow predictions for 3D digital rock images. We trained our …
3D reconstruction of digital cores based on a model using generative adversarial networks and variational auto-encoders
T Zhang, P Xia, F Lu - Journal of Petroleum Science and Engineering, 2021 - Elsevier
The digitalization of cores, namely the reconstruction of digital cores, is a method to reflect
the real internal structures of cores by reconstructing the microstructural information and …
the real internal structures of cores by reconstructing the microstructural information and …
Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks
A Valsecchi, S Damas, C Tubilleja, J Arechalde - Neurocomputing, 2020 - Elsevier
Micro computed tomography (CT) provides petrophysics laboratories with the ability to
image three dimensional porous media at pore scale. However, evaluating flow properties …
image three dimensional porous media at pore scale. However, evaluating flow properties …