Ensemble machine learning and mechanism-data fusion models successfully predict permeability in heterogeneous carbonate rocks using basic pore structure parameters.
Researchers have developed an Efficient Adaptive Aggregation Network that enhances two-dimensional digital rock image super-resolution while minimizing computational demands and ...
An integrated seismic and petrophysical evaluation of Egypt's El-Obayed Field demonstrates how fault geometry and shale gouge ratios govern Jurassic ...
Researchers evaluated six machine learning models to reconstruct missing acoustic well logs across onshore and offshore wells in New Zealand's ...
A multiscale study of four Chinese shales combines SEM imaging, dual-gas adsorption, and fractal analysis to show that organic matter ...
An enhanced color-gradient lattice Boltzmann model reproduced primary drainage in a micro-CT imaged sandstone plug, with simulated final oil saturations ...
A controlled benchmark of 17 pretrained convolutional neural networks on 52,706 micro-CT rock slices found DenseNet architectures delivered the most ...
A multi-branch deep-learning model reached 98.74% accuracy for oil-presence classification in a held-out synthetic reservoir dataset, but field validation remains ...
A study of the Longmaxi Formation in the Yongchuan area of the Sichuan Basin shows that varying intensities of tectonic ...
Researchers have released the first standardized open-access database of geochemical, petrophysical, and mechanical rock properties for Germany's crystalline basement, compiling ...
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