Search Results (82)

Thermal wick-debinding, commonly used in low-pressure injection molding, remains challenging due to complex interactions between binder transport, capillary forces, and thermal effects. This study presents a numerical simulation of binder removal kinetics by coupling Darcy’s law with the Phase Transport in Porous Media interface in COMSOL Multiphysics. The model was validated and subsequently used to study the influence of key debinding parameters. Contrary to the Level Set method, which predicts isolated binder clusters, the Multiphase Flow in Porous Media method proposed in this work more accurately reflects the physical behavior of the process, capturing a continuous binder extraction throughout the green part and a uniform binder distribution within the wicking medium. The model successfully predicted the experimentally observed decrease in binder saturation with increasing debinding temperature or time, with deviation limited 3–10 vol. % (attributed to a mandatory brushing operation, which may underestimate the residual binder mass). The model was then used to optimize the debinding process: for a temperature of 100 °C and an inter-part gap distance of 5 mm, the debinding time was minimized to 7 h. These findings highlight the model’s practical utility for process design, offering a valuable tool for determining optimal debinding parameters and improving productivity. Full article

The dynamic non-equilibrium effect (DNE) describes the non-unique character of saturation–capillary pressure relationships observed under static, steady-state, or monotonic hydrodynamic conditions. Macroscopically, the DNE manifests as variations in soil hydraulic characteristic curves arising from varying hydrodynamic testing conditions and is fundamentally governed by soil matrix particle size distribution. Changes in the DNE across porous media with discrete particle size fractions are investigated via stepwise drying experiments. Through quantification of saturation–capillary pressure hysteresis and DNE metrics, three critical signatures are identified: (1) the temporal lag between peak capillary pressure and minimum water saturation; (2) the pressure gap between transient and equilibrium states; and (3) residual water saturation. In the four experimental sets, with the finest material (Test 1), the peak capillary pressure consistently precedes the minimum water saturation by up to 60 s. Conversely, with the coarsest material (Test 4), peak capillary pressure does not consistently precede minimum saturation, with a maximum lag of only 30 s. The pressure gap between transient and equilibrium states reached 14.04 cm H₂O in the finest sand, compared to only 2.65 cm H₂O in the coarsest sand. Simultaneously, residual water saturation was significantly higher in the finest sand (0.364) than in the coarsest sand (0.086). The results further reveal that the intensity of the DNE scales inversely with particle size and linearly with wetting phase saturation (Sw), exhibiting systematic decay as Sw decreases. Coarse media exhibit negligible hysteresis due to suppressed capillary retention; this is in stark contrast with fine sands, in which the DNE is observed to persist in advanced drying stages. These results establish pore geometry and capillary dominance as fundamental factors controlling non-equilibrium fluid dynamics, providing a mechanistic framework for the refinement of multi-phase flow models in heterogeneous porous systems. Full article

Relative permeability is a critical parameter governing multiphase fluid flow through porous media, significantly impacting recovery efficiency and CO₂ sequestration potential in geological reservoirs. Accurately evaluating relative permeability in heterogeneous reservoirs remains challenging due to spatially variable porosity and permeability distributions. This study presents a novel intelligent prediction approach for evaluating water-CO₂ relative permeability in heterogeneous porous media by integrating fluid properties, heterogeneity characteristics, and relative permeability measurements from uniform porous media. We established a comprehensive training dataset through systematic micromodel experiments that captured various heterogeneity patterns and fluid conditions. Using this dataset, we developed an Artificial Neural Network (ANN) model that achieved exceptional accuracy with a Mean Squared Error below 0.0025. The model was then applied to predict relative permeability in heterogeneous reservoirs using site-specific relative permeability data obtained from core experiments as input parameters. To validate our approach, we incorporated the predicted relative permeability values into Computer Modelling Group (CMG) reservoir simulations of CO₂ sequestration in saline aquifers. The simulation results demonstrated strong agreement with published literature, confirming the model’s predictive capability. This work provides a practical, efficient, and reliable methodology for predicting relative permeability in heterogeneous reservoirs, addressing a significant challenge in reservoir characterization and flow modeling. Full article

The lattice Boltzmann method (LBM) is widely applied to model the pore-scale two-phase flow of immiscible fluids through porous media, and one common variant of the LBM is the color gradient method (CGM). However, in the literature, many competing algorithms have been proposed for accomplishing different steps in the CGM. Therefore, this paper is the first in a series that aims to critically review and evaluate different algorithms and methodologies that have been proposed for use in the CGM. Specifically, in this paper, we (1) provide a brief introduction to the LBM and CGM that enables and facilitates consideration of more sophisticated topics subsequently; (2) compare three methods for modeling the behavior of fluids of moderately different viscosities; (3) compare two methods for calculating the color gradient; and (4) compare two methods for modeling external forces or accelerations acting upon the fluids of interest. These topics are selected for the first paper in the series because proper selection of these algorithms is necessary and sufficient to perform two common “benchmark” simulations, namely bubble tests and layered Poiseuille flow. Future papers in the series will build upon these topics, considering more challenging conditions or phenomena. By systematically reviewing key aspects, features, capabilities, and limitations of the CGM, this series of papers will extend our collective ability to apply the method to a variety of important fluid flow problems in geosciences and engineering. Full article

This paper extends the multipoint flux approximation (MPFA-O) method to model coupled pressure and saturation dynamics in subsurface reservoirs with heterogeneous anisotropic permeability and non-K-orthogonal grids. The MPFA method is widely used for reservoir simulation to address the limitations of the two-point flux approximation (TPFA), particularly in scenarios involving full-tensor permeability and strong anisotropy. However, the MPFA-O method is known to suffer from spurious oscillations and numerical instability, especially in high-anisotropy scenarios. Existing stability-enhancing techniques, such as optimal quadrature schemes and flux-splitting methods, mitigate these issues but are computationally expensive and do not always ensure monotonicity or oscillation-free solutions. Building upon prior advancements in the MPFA-O method for pressure equations, this work incorporates the saturation equation to enable the simulation of a coupled multiphase flow in porous media. A unified framework is developed to address stability challenges associated with the tight coupling of pressure and saturation fields while ensuring local conservation and accuracy in the presence of full-tensor permeability. The proposed method introduces stability-enhancing modifications, including a local rotation transformation, to mitigate spurious oscillations and preserve physical principles such as monotonicity and the maximum principle. Numerical experiments on heterogeneous, anisotropic domains with non-K-orthogonal grids validate the robustness and accuracy of the extended MPFA-O method. The results demonstrate improved stability and performance in capturing the complex interactions between pressure and saturation fields, offering a significant advancement in subsurface reservoir modeling. This work provides a reliable and efficient tool for simulating coupled flow and transport processes, with applications in CO₂ storage, hydrogen storage, geothermal energy, and hydrocarbon recovery. Full article

This study systematically investigates the formation mechanism and development characteristics of the “foamy oil” phenomenon during pressure depletion development of high-viscosity crude oil through a combination of physical experiments and numerical simulations. Using Venezuelan foamy oil as the research subject, an innovative heterogeneous pore-etched glass model was constructed to simulate the pressure depletion process, revealing for the first time that bubble growth predominantly occurs during the migration stage. Experimental results demonstrate that heavy components significantly delay degassing by stabilizing gas–liquid interfaces, while the continuous gas–liquid diffusion effect explains the unique development characteristics of foamy oil—high oil recovery and delayed phase transition—from a microscopic perspective. A multi-scale coupling analysis method was established: molecular-scale simulations were employed to model component diffusion behavior. By improving the traditional Volume of Fluid (VOF) method and introducing diffusion coefficients, a synergistic model integrating a single momentum equation and fluid volume fraction was developed to quantitatively characterize the dynamic evolution of bubbles. Simulation results indicate significant differences in dominant controlling factors: oil phase viscosity has the greatest influence (accounting for ~50%), followed by gas component content (~35%), and interfacial tension the least (~15%). Based on multi-factor coupling analysis, an empirical formula for bubble growth incorporating diffusion coefficients was proposed, elucidating the intrinsic mechanism by which heavy components induce unique development effects through interfacial stabilization, viscous inhibition, and dynamic diffusion. This research breaks through the limitations of traditional production dynamic analysis, establishing a theoretical model for foamy oil development from the perspective of molecular-phase behavior combined with flow characteristics. It not only provides a rational explanation for the “high oil production, low gas production” phenomenon but also offers theoretical support for optimizing extraction processes (e.g., gas component regulation, viscosity control) through quantified parameter weightings. The findings hold significant scientific value for advancing heavy oil recovery theory and guiding efficient foamy oil development. Future work will extend to studying multiphase flow coupling mechanisms in porous media, laying a theoretical foundation for intelligent control technology development. Full article

In the mixing zone, where submarine groundwater carrying ferrous iron [Fe(II)] meets seawater with dissolved oxygen (DO), the oxidative precipitation of Fe(II) occurs at the pore scale (nm~μm), and the resulting Fe precipitation significantly influences the seepage properties at the Darcy scale (cm~m). Previous studies have presented a challenge in upscaling fluid dynamics from a small scale to a large scale, thereby constraining our understanding of the spatiotemporal variations in flow paths as porous media evolve. To address this limitation, this study simulated subsurface mixing by injecting Fe(II)-rich freshwater into a DO-rich saltwater flow within a custom-designed syringe packed with glass beads. Micro-computed tomography imaging at the representative elementary volume scale was utilized to track the development of Fe precipitates over time and space. Experimental observations revealed three distinct stages of Fe hydroxides and their effects on the flow dynamics. Initially, hydrous Fe precipitates were characterized by a low density and exhibited mobility, allowing temporarily clogged pathways to intermittently reopen. As precipitation progressed, the Fe precipitates accumulated, forming interparticle bonding structures that redirected the flow to bypass clogged pores and facilitated precipitate flushing near the syringe wall. In the final stage, a notable reduction in the macroscopic capillary number from 3.0 to 0.05 indicated a transition from a viscous- to capillary-dominated flow, which led to the construction of ramified, tortuous flow channels. This study highlights the critical role of high-resolution imaging techniques in bridging the gap between pore-scale and continuum-scale analyses of multiphase flows in hydrogeochemical processes, offering valuable insights into the complex groundwater–seawater mixing. Full article

Acid fracturing is essential in enhancing recovery efficiency, especially within carbonate reservoirs. Although extensive studies have been conducted on hydraulic fracturing, understanding the intricate dynamics between acid–rock reactions and fracture propagation in heterogeneous layered reservoirs remains limited. This study employs a comprehensive coupled hydro-mechanical-chemical flow framework to investigate acid fracturing processes in layered geological formations. The model incorporates a two-stage homogenization approach to account for rock heterogeneity, a dual-scale continuum framework for fluid flow and acid transport, and a phase field method for examining fracture propagation. We thoroughly examine how treatment parameters, particularly acid concentration and injection rate, affect fracture propagation modes. The analysis identifies three distinct propagation patterns: crossing, diversion, and arresting. These are influenced by the interplay between pressure buildup and wormhole formation. Initially, higher acid concentration aids in fracture crossing by lowering the peak pressure required for initiation, but excessive concentration results in arresting because it causes extensive wormhole development, which reduces fluid pressure. Similarly, the injection rate plays a crucial role in fracture movement across layer interfaces, with moderate rates optimizing propagation by balancing pressure and wormhole growth. This comprehensive modeling framework serves as a valuable prediction and control tool for acid fracture behavior in complex layered formations. Full article

Efficient and economical hydrocarbon extraction relies on a clear understanding of fluid flow dynamics in subsurface reservoirs, where multiphase flow in porous media poses complex modeling challenges. Traditional numerical methods for solving the governing partial differential equations (PDEs) provide effective solutions but struggle with the high computational demands required for accurately capturing fine-scale flow dynamics. In response, this study introduces a physics-informed generative adversarial network (GAN) framework for addressing the Buckley–Leverett (B-L) equation with non-convex flux functions. The proposed framework consists of two novel configurations: a Physics-Informed Generator GAN (PIG-GAN) and Dual-Informed GAN (DI-GAN), both of which are rigorously tested in forward and inverse problem settings for the B-L equation. We assess model performance under noisy data conditions to evaluate robustness. Our results demonstrate that these GAN-based models effectively capture the B-L shock front, enhancing predictive accuracy while embedding fluid flow equations to ensure model interpretability. This approach offers a significant advancement in modeling complex subsurface environments, providing an efficient alternative to traditional methods in fluid dynamics applications. Full article

Acid fracturing is a crucial stimulation technique to enhance hydrocarbon recovery in carbonate reservoirs. However, the interaction between acid fractures and natural fractures remains complex due to the combined effects of mechanical, chemical, and fluid flow processes. This study extends a previously developed hydro-mechano-reactive flow coupled model to analyze these interactions, focusing on the influence of acid dissolution. The model incorporates reservoir heterogeneity and simulates various scenarios, including different stress differences, approaching angles, injection rates, and acid concentrations. Numerical simulations reveal distinct propagation modes for acid and hydraulic fractures, highlighting the significant influence of acid dissolution on fracture behavior. Results show that hydraulic fractures are more likely to cross natural fractures, whereas acid fractures tend to be arrested due to wormhole formation. Increasing stress differences and approaching angles promote fracture crossing, while lower angles favor diversion into natural fractures. Higher injection rates facilitate fracture crossing by increasing pressure accumulation, but excessive acid concentrations hinder fracture initiation due to enhanced wormhole formation. The study demonstrates the importance of tailoring fracturing treatments to specific reservoir conditions, optimizing parameters to enhance fracture propagation and reservoir stimulation. These findings contribute to a deeper understanding of fracture mechanics in heterogeneous reservoirs and offer practical implications for improving the efficiency of hydraulic fracturing operations in unconventional reservoirs. Full article

A physics-informed convolutional neural network (PICNN) is proposed to simulate two-phase flow in porous media with time-varying well controls. While most PICNNs in the existing literature worked on parameter-to-state mapping, our proposed network parameterizes the solutions with time-varying controls to establish a control-to-state regression. Firstly, a finite volume scheme is adopted to discretize flow equations and formulate a loss function that respects mass conservation laws. Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations so no additional loss term is needed. The network architecture comprises two parallel U-Net structures, with network inputs being well controls and outputs being the system states (e.g., oil pressure and water saturation). To capture the time-dependent relationship between inputs and outputs, the network is well designed to mimic discretized state-space equations. We train the network progressively for every time step, enabling it to simultaneously predict oil pressure and water saturation at each timestep. After training the network for one timestep, we leverage transfer learning techniques to expedite the training process for a subsequent time step. The proposed model is used to simulate oil–water porous flow scenarios with varying reservoir model dimensionality, and aspects including computation efficiency and accuracy are compared against corresponding numerical approaches. The comparison with numerical methods demonstrates that a PICNN is highly efficient yet preserves decent accuracy. Full article

Understanding multiphase flow through porous media is integral to geologic carbon storage or hydrogen storage. The current modelling framework assumes each fluid present in the subsurface flows in its own continuously connected pathway. The restriction in flow caused by the presence of another fluid is modelled using relative permeability functions. However, dynamic fluid interfaces have been observed in experimental data, and these are not accounted for in relative permeability functions. In this work, we explore the occurrence of fluid fluctuations in the context of sizes, locations, and frequencies by altering the viscosity ratio for two-phase flow. We see that the fluctuations alter the connectivity of the fluid phases, which, in turn, influences the relative permeability of the fluid phases present. Full article

In the article, we propose a combination method based on the multigrid method and constraint data to solve the inverse problem in the context of the nonlinear convection–diffusion equation in the multiphase porous media flow. The inverse problem consists of a data-fitting term involving the discretization of a direct problem, a constraint term concerning the incorporation of constraint data, and a regularization term dealing with the improvement of stability. A multigrid method, which is specialized for large-scale problems and works by keeping the consistence of objective functionals between different grids, is applied in the process of inversion. Based on the numerical results, the proposed combination method has the advantages of fast calculation, high precision, good stability, and strong anti-noise ability in computation. It obtains good performance under various noise levels, as well as outperforming any one method used alone. Full article

Advancing the modeling of evaporation and salt precipitation is essential in CO₂ storage processes in aquifers. OpenFOAM provides a platform for computational fluid dynamics (CFD) modeling with its open-source C++ object-oriented architecture that can especially be used in the development of fluid flow models in porous media. Some OpenFOAM packages have been developed in this area, and their codes are available for use. Despite this, the existing OpenFOAM literature does not include a model that incorporates multicomponent interactions in multi-phase flow systems, referred to as compositional modeling, at the Darcy scale. This existing gap is addressed in this paper, where a new simple model in OpenFOAM is introduced that aims to model the interaction of CO₂ and H₂O components in CO₂ storage processes in aquifers at the Darcy scale. The model, named compositionalIGFoam, incorporates a compositional solver by extending the impesFoam solver of the porousMultiphaseFoam package, while assuming some simplifications, to account for CO₂/water mutual dissolution, relevant to carbon capture and storage (CCS) processes in aquifers. The functionality of the compositionalIGFoam solver was assessed by showcasing its ability to reproduce the outcomes of existing examples. In addition to that, the process of gas injection into a water-saturated core sample was simulated using the developed model to mimic CO₂ injection into aquifers. The CMG-GEM commercial compositional simulator was used to compare its results with the coreflood model of this study. Phenomenal agreement was achieved with the GEM model, showing only 1.8% and 0.4% error for both components. This confirms the accuracy and reliability of the developed model. In conclusion, this study enhances the state of the art in porous media modeling using OpenFOAM 10, providing a valuable tool for examining fluid interactions in subsurface environments, especially within the context of CCS processes. Full article