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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization
Jan 27, 2026Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke +5Porous Media Lithology Classification
Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom · Department of Geoscience & Engineering, Technical University of Delft, Delft, The Netherlands · PETRONAS Research Sdn Bhd, Bangi, Selangor, Malaysia
InSituRes: A Physics-Informed Same-Grid Model for Enhanced Dynamic X-ray Micro-CT Reconstructions
Aug 25, 2025Qinyi Tian, Andrea Bisciotti, Soniya Tiwari +2X-Ray Cone-Beam Computed Tomography
Department of Civil & Environmental Engineering, Duke University, Durham, North Carolina, 27708, USA · Department of Physics and Earth Sciences, University of Ferrara, Via Saragat 1, Ferrara 44141, Italy · Department of Mechanical Engineering & Materials Science, Duke University, Durham, North Carolina, 27708, USA
EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation
Aug 22, 2025Nan Wang, Zhiyi Xia, Yiming Li +7Electron Microscopy Porous Media
DP Technology, Beijing, China

Jan 27, 2026, physics.geo-phJ/K move · Enter open · S save

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes+4

Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom · Department of Geoscience & Engineering, Technical University of Delft, Delft, The Netherlands · PETRONAS Research Sdn Bhd, Bangi, Selangor, Malaysia

Porous Media Lithology Classification Cone-Beam Computed Tomography Deep Learning Framework Data Assimilation

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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 exhibit pore size distribution across scales, hindering the comprehensive investigation with conventional X-ray CT images. Imaging samples at both macro and micro-scales (multi-scale imaging) proved to be a viable option in this context. However, multi-scale imaging faces two key limitations: the trade-off between field of view and voxel size necessitates resource-intensive imaging, while multi-scale multi-physics numerical simulations on resulting digital models incur prohibitive computational costs. To address these challenges, we propose a machine learning-enhanced data assimilation framework that leverages experimental drainage relative permeability measurements to achieve efficient characterization of micro-scale structures, delivering a data-driven solution toward a high-fidelity multiscale digital rock modeling. We train a dense neural network (DNN) as a proxy to a multi-scale pore network simulator and couple it with an ensemble smoother with multiple data assimilation (ESMDA) algorithm. DNN-ESMDA framework simultaneously infers the CO2-brine drainage relative permeability of microporosity phases with associated uncertainty estimation, revealing the relative importance of each rock phase and guiding future characterization. Our DNN-ESMDA framework achieves a computational speedup, reducing inference time from thousands of hours to seconds compared with the usage of conventional multiscale numerical simulation. Given this computational efficiency and applicability, the machine learning-enhanced ESMDA framework presents a generalizable approach for characterizing multiscale carbonate rocks.

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Latest papers 63

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

InSituRes: A Physics-Informed Same-Grid Model for Enhanced Dynamic X-ray Micro-CT Reconstructions

EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation