Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
[HTML][HTML] A survey on GANs for computer vision: Recent research, analysis and taxonomy
In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
Generative adversarial networks review in earthquake-related engineering fields
Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …
via generative adversarial networks (GANs), represents an innovative, engaging, and …
Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media
The permeability of complex porous materials is of interest to many engineering disciplines.
This quantity can be obtained via direct flow simulation, which provides the most accurate …
This quantity can be obtained via direct flow simulation, which provides the most accurate …
Semi-supervised learning for seismic impedance inversion using generative adversarial networks
B Wu, D Meng, H Zhao - Remote Sensing, 2021 - mdpi.com
Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy
Abstract Machine learning supports prediction and inference in multivariate and complex
datasets where observations are spatially related to one another. Frequently, these datasets …
datasets where observations are spatially related to one another. Frequently, these datasets …
Automatic responsive-generation of 3D urban morphology coupled with local climate zones using generative adversarial network
Decoupling the intricate relationship between three-dimensional (3D) urban morphology
and local climate is paramount importance in the realm of adaptive urban planning …
and local climate is paramount importance in the realm of adaptive urban planning …
[HTML][HTML] Latent diffusion model for conditional reservoir facies generation
Creating accurate and geologically realistic reservoir facies based on limited measurements
is crucial for field development and reservoir management, especially in the oil and gas …
is crucial for field development and reservoir management, especially in the oil and gas …
Reservoir prediction based on closed-loop CNN and virtual well-logging labels
Reservoir prediction is a significant issue in seismic interpretation, and it is difficult to reach a
tradeoff point for the reservoir prediction accuracy and spatial continuity. Nowadays, though …
tradeoff point for the reservoir prediction accuracy and spatial continuity. Nowadays, though …
Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models
Well-log interpretation estimates in situ rock properties along well trajectory, such as
porosity, water saturation, and permeability, to support reserve-volume estimation …
porosity, water saturation, and permeability, to support reserve-volume estimation …