Relative phase permeability is an important characteristic of multiphase flow in porous media. Its assessment is an urgent issue when the pore structure changes due to rock dissolution. This article examines the effect of carbonate rock dissolution on two-phase flow based on images obtained by X-ray microcomputed tomography with a spatial resolution of ~18 µm. The characteristics of the two-phase flow were calculated through pore network modeling. The studies were conducted on 20 sub-volumes, which were extracted from cylindrical samples A and B with permeabilities of 0.72 and 0.29 D. HCl solutions (12% and 18%) were injected into samples A and B at a rate of 8 and 2 mL/min, respectively. Due to rock dissolution, the porosity and absolute permeability of the sub-volumes increased by 1.1–33% and 44–368%, respectively. Due to dissolution, the residual oil and water saturations decreased by 20–46% and 25–60%, respectively. These results showed that an increase in absolute permeability led to a significant reduction in residual oil and water saturations. These results also demonstrated that rock dissolution resulted in a change in the spatial heterogeneity of the relative phase permeabilities. The spatial heterogeneity increased in sample A after rock dissolution, while in sample B, it decreased.

This work develops and systematically evaluates a physics-informed neural network (PINN) solver for the fully coupled, time-dependent Muskat–Leverett system with capillarity modeled in the pressure equation. A single shallow–wide multilayer perceptron jointly predicts wetting pressure and water saturation; physical capillary pressure regularizes the saturation front, while a small numerical diffusion term in the saturation residual acts as a training stabilizer rather than a shock-capturing device. To guarantee admissible states in stiff regimes, we introduce a saturation soft-clamping head enforcing $0<{\mathrm{S}}_{\mathrm{w}}<1$ and activate it selectively for stiff mobility ratios. Using IMPES solutions as reference, we perform a sensitivity study over network depth and width, interior collocation and boundary data density, mobility ratio, and injection pressure. Shallow-wide networks (10 layers × 50 neurons) consistently outperform deeper architectures, and increasing interior collocation points from 5000 to 50,000 reduces mean saturation error by about half, whereas additional boundary data have a much weaker effect. Accuracy is highest at an intermediate mobility ratio and improves monotonically with higher injection pressure, which sharpens yet better conditions the front. Across all regimes, pressure trains easily while saturation determines model selection, and the PINN serves as a physics-consistent surrogate for what-if studies in two-phase porous-media flow.

Droplet generation in microfluidic T-junctions is a key process in various chemical and biomedical applications requiring precise size and frequency control. This study presents a numerical investigation of pulsation-controlled droplet formation using a two-phase incompressible laminar flow model with constant surface tension and defined wettability. Simulations were conducted in COMSOL Multiphysics employing the Level Set method, and the model was validated against the benchmark data of Bashir et al., accurately reproducing droplet pinch-off time and morphology under steady co-flow conditions. Pulsatile inlet velocity was then introduced to analyze its influence on droplet dynamics. Results show that at frequencies between 35 and 60 Hz, droplet generation becomes synchronized with the pulsation cycle, producing one droplet per period. Beyond 60 Hz, synchronization is lost, leading to irregular breakup and loss of droplet size control. The droplet length exhibited an approximately linear dependence on pulsation frequency, indicating predictable and tunable droplet formation. These findings demonstrate that simple modulation of the dispersed-phase velocity enables droplet-on-demand operation and robust control of droplet size and generation rate in standard microfluidic T-junctions.

This study investigates how pulsatile injection influences two-phase displacement efficiency in a pore-scale micromodel, with emphasis on the roles of porosity, amplitude, and frequency. Simulations were performed using a conservative level-set formulation in COMSOL Multiphysics across three porosity levels (φ ≈ 0.75, 0.50, 0.30) and a range of amplitudes (25–75%) and frequencies (0–200 Hz), with fixed fluid properties and wettability. In the baseline (non-pulsed) regime, residual saturation decreased with increasing inlet velocity but reached a plateau, indicating a velocity-limited mobilization. Superimposing sinusoidal pulsations led to improved displacement only within finite frequency bands. For each porosity, a “working window” emerged, where residual saturation reached a minimum: this optimum shifted toward lower frequencies and higher amplitudes with decreasing φ. These trends were quantified using the normalized residual saturation $S_{\mathrm{res}}/S_{\mathrm{ref}}$ and linked to the dimensionless Strouhal number $\mathrm{St}$, defined via the capillary time scale. Phase maps and velocity fields confirmed that at optimal conditions, pulsations activated transverse throats and suppressed capillary bridges, while excessive frequencies led to inefficient re-entrapment. A fixed observation time was used to enable consistent comparison across regimes. The findings delineate the parameter ranges under which pulsations yield tangible benefit and suggest practical guidance for tuning flow modulation based on pore structure.

Dissolution increases the porosity and permeability of carbonate rock, but its effect on residual oil saturation and the dependence of this effect on wettability remain poorly constrained. This work evaluates how dissolution-induced changes in pore space affect oil displacement by water under water-wet, neutral-wet, and oil-wet conditions. Four carbonate cores, composed of 99–100% calcite, were treated with 12% HCl and scanned by X-ray microtomography before and after dissolution at a resolution of 18 μm. Two cubic sub-volumes were extracted from each sample at identical coordinates in both states, giving eight before-and-after pairs. Pore networks were built in pnextract, and two-phase flow was simulated in pnflow at contact angles of 20–30°, 80–90°, and 100–110°. The paired design allowed each sub-volume to serve as its own control, while running the three scenarios on identical networks separated the contribution of structure from that of wettability. Dissolution reshaped the pore space of all eight sub-volumes, enlarging pores, raising network connectivity, reducing tortuosity and increasing absolute permeability, although the magnitude of these changes was uneven, and in one sub-volume permeability did not increase at all. The reduction in residual oil saturation was statistically significant under water-wet and neutral-wet conditions but absent under oil-wet conditions, despite identical structural changes in all three cases. Oil is retained by different mechanisms in water-wet and oil-wet rock, so the same changes in pore space act on displacement in different ways. In these simulations dissolution improved oil displacement only while water remained the wetting phase, which indicates that permeability enhancement alone may not lead to a lower residual oil saturation under oil-wet conditions and that wettability should be considered when evaluating post-acidizing performance.

The Caspian Sea’s unique environment and intense hydrocarbon extraction make it a high-risk, understudied region for oil spill modelling. This review assesses the physical, chemical, and biological processes governing oil spill transport and fate, and evaluates the principal numerical tools available for the Caspian Sea context. The weathering processes are reviewed from foundational formulations to operational implementations. Key research challenges identified include the absence of photo-oxidation from operational models, limited laboratory data for Caspian crude oil types, and simplified biodegradation parameterizations. Hydrodynamic forcing uncertainty, arising from the lack of a dedicated operational ocean model, remains the dominant source of trajectory forecast error. Seven operational oil spill modelling tools and the ROMS hydrodynamic platform are reviewed. Only OSCAR and MIKE 21 have documented applications to the Caspian Sea, representing a significant regional gap. ROMS is identified as the most suitable hydrodynamic platform for future operational forecasting. Finally, the integration of machine learning and deep learning methods, including neural network trajectory prediction and SAR detection, is discussed as a promising frontier for improving forecast accuracy in this data-sparse environment.