TY - JOUR

T1 - Automatic subsecond analysis of fluid dynamics in porous media via fast synchrotron imaging

AU - Gorenkov, Ivan

AU - Fokin, Mikhail

AU - Duchkov, Anton

N1 - Ivan Gorenkov, Mikhail Fokin, Anton Duchkov, Automatic subsecond analysis of fluid dynamics in porous media via fast synchrotron imaging, International Journal of Multiphase Flow, Volume 203, 2026, 105848, ISSN 0301-9322, https://doi.org/10.1016/j.ijmultiphaseflow.2026.105848. This work was supported as part of the research program on the topic ‘‘Development of infrastructure and methods for the use of synchrotron radiation to optimize the extraction of hard-to-recover oil and gas reserves’’ under Agreement No. 075-15-2025-510 dated May 30, 2025. The authors gratefully acknowledge V.V. Nikitin for the contribution to the synchrotron X-ray micro-CT data acquisition at the 2-BM beamline of the Advanced Photon Source (APS). We also thank him for the valuable collaboration during the initial stages of this research.

PY - 2026/9

Y1 - 2026/9

N2 - Synchrotron-based computed microtomography is an efficient tool for investigating pore-scale dynamics of multiphase flow in porous media. It provides temporal resolution down to several seconds per 3D image. However, the multiphase flow in porous media has a sporadic nature and happens at subsecond timescales, resulting in motion artifacts in tomographic reconstructions. We address this limitation by extracting information from tomographic projections that capture images of the hydrodynamic system with a resolution of tens of milliseconds. We propose using a neural network to detect pore-scale displacement events by analyzing the differences between successive projections. Our neural-network model was trained on a synthetic dataset representing low-contrast tomography data. The model was applied to synchrotron tomographic data of methane gas-hydrate formation within a crushed coal sample where cryogenic suction caused rapid sporadic fluid-flow events exhibiting dynamics reminiscent of Haines jumps. These events, with durations ranging from 100 ms to over 2 s, were separated by stabilization intervals of various lengths. We observed heterogeneity in the spatial distribution of the fast-flow events, which predominantly occurred near metal inclusions. The approach outperformed traditional segmentation methods, especially in conditions of low material contrast, low flow rate, or small pore sizes. Overall, the proposed approach serves as an effective tool for identifying the specific time intervals of pore-scale dynamics during extended tomographic imaging. The resulting data provides insights into local flow regimes as well as their characteristic temporal and spatial scales.

AB - Synchrotron-based computed microtomography is an efficient tool for investigating pore-scale dynamics of multiphase flow in porous media. It provides temporal resolution down to several seconds per 3D image. However, the multiphase flow in porous media has a sporadic nature and happens at subsecond timescales, resulting in motion artifacts in tomographic reconstructions. We address this limitation by extracting information from tomographic projections that capture images of the hydrodynamic system with a resolution of tens of milliseconds. We propose using a neural network to detect pore-scale displacement events by analyzing the differences between successive projections. Our neural-network model was trained on a synthetic dataset representing low-contrast tomography data. The model was applied to synchrotron tomographic data of methane gas-hydrate formation within a crushed coal sample where cryogenic suction caused rapid sporadic fluid-flow events exhibiting dynamics reminiscent of Haines jumps. These events, with durations ranging from 100 ms to over 2 s, were separated by stabilization intervals of various lengths. We observed heterogeneity in the spatial distribution of the fast-flow events, which predominantly occurred near metal inclusions. The approach outperformed traditional segmentation methods, especially in conditions of low material contrast, low flow rate, or small pore sizes. Overall, the proposed approach serves as an effective tool for identifying the specific time intervals of pore-scale dynamics during extended tomographic imaging. The resulting data provides insights into local flow regimes as well as their characteristic temporal and spatial scales.

KW - Deep learning

KW - Methane hydrate physics

KW - Pore-scale flow

KW - Subsecond fluid dynamics

KW - X-ray synchrotron imaging

KW - синхротронная рентгеновская визуализация

KW - фильтрация на уровне пор

KW - сверхбыстрая динамика флюидов

KW - физика гидратов метана

KW - глубокое обучение

UR - https://www.mendeley.com/catalogue/73a48b36-cd2f-39c6-ad34-a8bf5b35fc1b/

UR - https://www.scopus.com/pages/publications/105047148907

U2 - 10.1016/j.ijmultiphaseflow.2026.105848

DO - 10.1016/j.ijmultiphaseflow.2026.105848

M3 - Article

VL - 203

JO - International Journal of Multiphase Flow

JF - International Journal of Multiphase Flow

SN - 0301-9322

M1 - 105848

ER -