Pore-network modeling of flow in shale nanopores: Network structure, flow principles, and computational algorithms
Hydrocarbons in subsurface nanoporous media, such as shale, are promising energy
resources to compensate for the shortage of conventional reservoirs. Pore-network …
resources to compensate for the shortage of conventional reservoirs. Pore-network …
Reconstruction, optimization, and design of heterogeneous materials and media: Basic principles, computational algorithms, and applications
M Sahimi, P Tahmasebi - Physics Reports, 2021 - Elsevier
Modeling of heterogeneous materials and media is a problem of fundamental importance to
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
Reconstruction of porous media from extremely limited information using conditional generative adversarial networks
Porous media are ubiquitous in both nature and engineering applications. Therefore, their
modeling and understanding is of vital importance. In contrast to direct acquisition of three …
modeling and understanding is of vital importance. In contrast to direct acquisition of three …
Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods
Permeability prediction is crucial in shale gas and CO 2 geological sequestration. However,
the intricate pore structure complicates the prediction of permeability. Machine learning (ML) …
the intricate pore structure complicates the prediction of permeability. Machine learning (ML) …
An Overview of Geological CO2 Sequestration in Oil and Gas Reservoirs
A tremendous amount of fossil fuel is utilized to meet the rising trend in the world's energy
demand, leading to the rising level of CO2 in the atmosphere and ultimately contributing to …
demand, leading to the rising level of CO2 in the atmosphere and ultimately contributing to …
An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning
Stochastically reconstructing a three-dimensional (3D) structure of porous media from a
given two-dimensional (2D) image is an outstanding problem. For such problem, despite …
given two-dimensional (2D) image is an outstanding problem. For such problem, despite …
Recent advances in multiscale digital rock reconstruction, flow simulation, and experiments during shale gas production
The complex and multiscale nature of shale gas transport imposes new challenges to the
already well-developed techniques for conventional reservoirs, especially digital core …
already well-developed techniques for conventional reservoirs, especially digital core …
A deep-learning-based geological parameterization for history matching complex models
A new low-dimensional parameterization based on principal component analysis (PCA) and
convolutional neural networks (CNN) is developed to represent complex geological models …
convolutional neural networks (CNN) is developed to represent complex geological models …
CT-image of rock samples super resolution using 3D convolutional neural network
Computed Tomography (CT) imaging technique is widely used in geological exploration,
medical diagnosis and other fields. However, the resolution of CT images is usually limited …
medical diagnosis and other fields. However, the resolution of CT images is usually limited …
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 …