arXiv subjects

Yanghua Wang

Publications and source records attributed to Yanghua Wang.

5 recordsLinked to original sources

arXiv2026

Multiphase Reactive Transport in a Heterogeneous Flow Field: Channel Formation in Hydrocarbon-Bearing Carbonate Rock

Carbon capture, utilization and storage (CCUS) is a key approach for reducing anthropogenic CO2 emissions. Currently the vast majority of CO2 stored is injected into depleted hydrocarbon reservoirs. Reactive transport in hydrocarbon-bearing carbonate reservoirs is controlled not only by the balance between reactant delivery and surface reaction, but also by the heterogeneous pore-scale flow field created by pore structure and fluid distribution. Here, we used time-resolved micro-CT imaging, pore-network analysis, and direct numerical simulation to investigate channelized dissolution during injection of CO2-saturated brine into oil-bearing Ketton limestone at 0.5 mL/min. The system remained in a high Pe, low Da regime throughout the 180 min injection. Despite the advection-dominated flow regime, dissolution became strongly localized. Preferential flow pathways were already present originally because the heterogeneous pore structure and remaining-oil occupancy restricted brine flow to a subset of the connected pore space. Dissolution progressively amplified these pathways. The associated reduction in hydraulic resistance further focused flow, producing a continuous tortuous channel by 180 min. Meanwhile, the effective reaction rate was only 1.6*10^5 mol/m2s,, approximately one order of magnitude lower than the batch reaction rate, showing that rapid advective transport did not translate directly into rapid overall dissolution under multiphase conditions. These results demonstrate that channel formation arose from the coupling between strong reactant delivery and pore-scale flow-field heterogeneity, rather than from the bulk Pe Da regime alone. Accounting for flow-field heterogeneity is therefore important for predicting reactive transport and pore-structure evolution during CO2 injection into hydrocarbon-bearing carbonate reservoirs.

physics.flu-dyn↗

arXiv2026

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks

Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-preserving alternative to data-driven surrogate modelling in scientific machine learning. However, existing realizations remain largely confined to classical AI hardware and do not directly connect to quantum structured operator design. To bridge this gap, we introduce a \emph{Quantum Neural Physics} framework and develop a Hybrid Quantum-Classical CNN Multigrid Solver (HQC-CNNMG). The proposed method maps analytically prescribed stencil operators to local quantum convolutional primitives and embeds them within a classical multilevel W-cycle architecture, combining the operator-centric view of scientific ML with the numerical rigor of multigrid solvers. Using amplitude encoding together with the Linear Combination of Unitaries (LCU) and the Quantum Fourier Transform (QFT), the resulting local quantum operators admit logarithmic-depth implementation, with circuit depth scaling as $\mathcal{O}(\log K)$ for an encoded block of size $K$ under the idealized parallel circuit model considered here. Numerical experiments on Poisson, transient diffusion, convection--diffusion, and incompressible Navier--Stokes problems demonstrate numerical consistency, stable multilevel behaviour, and workflow-level feasibility on noiseless simulators. Comparisons with representative quantum linear solver paradigms further show that the main strength of HQC-CNNMG lies in its balanced trade-off among local circuit depth, numerical robustness, and compatibility with PDE structure, rather than in fully quantum global inversion.

quant-ph↗

arXiv2026

Time-Resolved Pore-Scale Imaging of Multiphase Dissolution during CO2-Saturated Brine Injection into a Carbonate: Competition between Hydrocarbon Mobilisation and Swelling

We present time-resolved pore-scale experiments in which CO2-saturated brine was injected into a water-wet Ketton limestone sample containing residual hydrocarbon under reservoir conditions (8 MPa, 50 °C) and monitored by 4D X-ray microtomography. Equivalent pore-network models were extracted at each scan time to track pore geometry, topology, and fluid occupancy, while fluid-fluid and fluid-rock interfacial areas and the effective reaction rate were determined from segmented images. The dissolution rate is non-monotonic in time and proceeds through three regimes, consistent with a shifting balance between hydrocarbon swelling and ganglion mobilisation, which control advective access to reactive surfaces. In the initial advection-dominated regime, pore-throat widening leads to ganglia mobilisation and efficient acidic brine delivery to reactive surfaces. The second, dissolution-inhibited regime is marked by up to two orders of magnitude reduction in effective reaction rate. Pore-network analysis shows that swollen hydrocarbon ganglia persistently occupy the largest throats throughout this regime. This occupancy is associated with a reorganisation of the advective flow field into preferential flow paths and stagnant zones. We interpret the rate suppression as primarily reflecting a path-dependent loss of advective access to reactive surfaces, with subordinate contributions from localised H+ depletion near ganglia and reduced near-wall mass transfer in widened flow paths. The inhibited state persists until hydrocarbon is displaced from the largest throats, after which, in the third stage, advective access improves and rock dissolution accelerates. These results show that the effective dissolution rate in residual-hydrocarbon-bearing carbonate depends dynamically on the competition between hydrocarbon swelling and ganglion mobilisation, governing advective access to surfaces.

physics.flu-dyn↗

arXiv2025

Pore-Scale Dynamics of Multiphase Reactive Transport in Water-Wet Carbonates under CO2-Acidified Brine Injection: Dissolution Patterns and Reaction Rates

Depleted carbonate hydrocarbon reservoirs are promising sites for geological CO2 storage, yet the presence of residual hydrocarbons introduces complex pore-scale interactions that influence the dynamics of solid dissolution. This study reveals how residual oil affects dissolution patterns and effective reaction rates during CO2-acidified brine injection into Ketton limestone under reservoir conditions. We combine time-resolved X-ray microtomography (micro-CT), core-flooding experiments, and direct numerical simulations to assess the impact of pore space heterogeneity, oil distribution and injection rate. We find that the coupling between pore structure, residual oil saturation and oil displacement control flow heterogeneity, reactive surface accessibility, dissolution patterns and the reaction rates. At low injection rate, dissolution by channel widening is enhanced by oil displacement. This mechanism is especially important when dissolution is suppressed by heterogeneity in the pore space and the residual oil. At high injection rates, a more uniform dissolution occurs and can be enhanced by re-mobilisation of oil blocking brine flow. Effective reaction rates in two-phase flow are found to be lower than in the the equivalent single-phase case and up to two orders of magnitude lower than the batch rates due to persistent transport limitations. These findings offer mechanistic insights into multiphase reactive transport in carbonates and highlight the need for accurate understanding of the impact of the hydrocarbon phase on reaction to improve predictions of CO2 storage efficiency.

physics.flu-dyn↗

arXiv2024

Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)

PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions. Deep learning models show potential in efficiency but still suffer from accuracy loss over time due to error accumulation. To address these challenges, we propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations, ensuring reliable operational forecasting. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system while maintaining comparable accuracy. This efficiency benefits ensemble forecasting, uncertainty analysis, and large-scale tasks.

cs.LG↗