Enhanced Oil Recovery Techniques

Neueste Paper

• Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model

  Ali Aouf, Eric Laloy, Bart Rogiers, Christophe De Vleeschouwer · 18. September 2026

  Characterizing the physical properties of clay and cementitious materials matters across many fields, from materials science to geological waste disposal. Property simulation typically calls for 3D imaging, which is expensive, not always accessible, and technically limited for certain materials. Rec…

• Property-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks

  Ali Sadeghkhani, Brandon Bennett, Arash Rabbani · 7. Juli 2026

  This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin section images. The key innovation lies in combining property-conditioned generation with 2D-to-3D reconstruction, elimina…

• PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

  Ali Sadeghkhani, Brandon Bennett, Masoud Babaei, Arash Rabbani · 30. Juni 2026

  Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogeneity causes extracted sub-images to deviate significantly from core-measured values. This challenge is compounded by data …

• Reservoir property image slices from the Groningen gas field for image translation and segmentation

  Abdulrahman Al-Fakih, Nabil Sariah, Ardiansyah Koeshidayatullah, SanLinn I. Kaka · 6. Mai 2026

  Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly available geological image datasets suitable for reproducible benchmarking remain limited. Here we describe a high-resolution dataset of reservoir-pro…

• GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction

  Yue Shi, Peng Wang, Mingzhe Yu, Yunlong Zhao, Li Liu, Gareth D Hatton, Yan Lyu, Liangxiu Han · 6. Mai 2026

  Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements for simultaneously modelling both the continuous pore morphology and the discrete pore-throat topology, the diffusion models require fully observed …

• SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images

  Rui Zhang, Xianzhi Song, Linqi Zhu, Branko Bijeljic, Gensheng Li, Martin J. Blunt · 30. April 2026

  Reliable segmentation of multiphase pore-scale X-ray images of rocks is necessary to quantify fluid saturation, connectivity, and interfacial geometry. However, current 3D segmentation methods are typically dataset-specific, requiring retraining or extensive fine-tuning whenever rock type, fluid pat…

• PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction

  Yizhuo Huang, Baoquan Sun, Haibo Huang · 14. April 2026

  This manuscript presents PoreDiT, a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales. Addressing the significant challenges in digital rock physics (DRP), particularly the trade-off between resolution and field-of-view (FOV), and the computational b…

• Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer

  Mohammad Nooraiepour · 19. März 2026

  Accurate prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample, fundamentally limiting large-scale uncertainty quantification and reservoir optimization workflows. A physics-inform…

• Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps

  Asif Hamid, Danish Rafiq, Shahkar Ahmad Nahvi, Mohammad Abid Bazaz · 11. März 2026

  Complex systems often show macroscopic coherent behavior due to the interactions of microscopic agents like molecules, cells, or individuals in a population with their environment. However, simulating such systems poses several computational challenges during simulation as the underlying dynamics va…

• Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs

  Ali Sadeghkhani, A. Assadi, B. Bennett, A. Rabbani · 11. März 2026

  Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditional Generative Adversarial Network (cGAN) framework for synthesizing realistic thin section images of carbonate rock fo…

• Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

  Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes, Sebastian Geiger, Muhammad Z. Kashim, Zainol A. A. Bakar, Kamaljit Singh · 10. Februar 2026

  Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage. X-ray computed tomography (X-ray CT) coupled with numerical simulations is commonly used to investigate the multiphase flow behaviors in carbonate rocks. Carbonates exhibi…

• Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures

  Sigurd Vargdal, Paula Reis, Henrik Andersen Sveinsson, Gaute Linga · 2. Dezember 2025

  Permeability is a central concept in the macroscopic description of flow through porous media, with applications spanning from oil recovery to hydrology. Traditional methods for determining the permeability tensor involving flow simulations or experiments can be time consuming and resource-intensive…