PoreStudio - First Cloud-Based Multiphase Reactive Flow Simulator
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A new 3D multiphase numerical capability is presented here for simulating multiphase flow regimes at all Mach numbers (M). The new method is a semi-implicit extension of the finite volume discrete equation method (DEM) of Chinnaya et al. (2004), which originally used explicit time-stepping. The capability is also developed to work with another extension of the DEM to moving grids for arbitrary Lagrangian-Eularian (ALE) methods detailed in Dunn (2011). Rather than solving all phase equations simultaneously, the DEM reduces the equations to a system of single-phase Riemann solves, where each phase has its own velocity and thermodynamic state. Exchanges between the phases are modeled through source terms accounting for the phase interactions. Furthermore, since the original multiphase scheme uses an explicit time-advancement scheme, it has time step restrictions dictated by the speed of sound, which limits the model's ability to simulate weakly compressible flows which typically need to be integrated for longer time periods. Here, we extend the current multiphase formulation by implementing a pressure-correcting step to enable implicit calculations and remove acoustic time step limitations. The new semi-implicit algorithm allows use of relatively large time steps compared to an explicit method. Validation and benefits of the new implicit time-step method are illustrated using several examples including weakly compressible flows and strong shock waves. The scheme presented here is general and may be used for a variety of applications which require capabilities for handling multiphase flow at a wide range of Mach numbers. However, the goal of this research is to simulate all stages of high energy explosions, including the shock formation (high Mach numbers) and evolution of the buoyant cloud (low Mach numbers).
For presentation at the AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
The water-gas relative permeability is an important parameter to characterize multiphase flow in sediments. To study the water-gas relative permeability of hydrate-bearing porous media, multiphase flow simulations were carried out at the pore scale using the lattice Boltzmann method. In this work, the effects of hydrate saturation and hydrate-growth habits on the water-gas relative permeability, which is scaled by the relative permeability considering the hydrate only, were evaluated in a two-dimensional porous medium. Results show that the increase of hydrate saturation causes the decrease of water-gas effective permeability as expected. However, the effect of hydrate saturation on the water-gas relative permeability is different from that of hydrate saturation on the water-gas effective permeability. The water-gas relative permeability increases with the increase of hydrate saturation in the pore-filling case. The water-gas relative permeability decreases with the increase of hydrate saturation in the grain-coating case. The wettability of solid phase has a different effect on the relative permeability of wetting phase and nonwetting phase. The Jamin effect (phase blocking) was observed and may exist in the production of gas from natural gas hydrate reservoirs. This seriously affects the multiphase flow characteristics. The changes of microscale fluid distribution effect the changes of water-gas relative permeability. The relationship between the water-gas relative permeability and the characterization parameters of microscale fluid distribution was analyzed.
This work presents a high-order numerical approach for particle-resolved simulations of disperse multiphase flows, where the Navier-Stokes equations for fluid flow are solved using a high-order spectral element method in the Eulerian framework, and the particle phase is directly simulated with a discrete element method. The coupling between particles and fluids is explicitly handled using an adapted direct-forcing immersed boundary method. Unlike the conventional schemes, a high-order barycentric Lagrange interpolation method and a Gaussian projection kernel are used to ensure accurate momentum exchange between local boundary points and surrounding fluid nodes in the framework of high-order fluid solver. Benchmark tests of increasing complexity are conducted to demonstrate the accuracy and efficiency of our method. Here, it is found that our approach exhibits an excellent convergence performance, as the fluid element/grid is refined and the number of boundary points increases. Compared to conventional low-order methods, the proposed high-order framework enables the use of substantially larger fluid elements while maintaining high accuracy in modeling fluid-particle interactions, owing to the enhanced resolution of high-order basis functions. Moreover, since the primary unknowns are stored at element or grid nodes, the high-order approach offers improved efficiency in both CPU memory usage and total computational cost.
A modified many-body dissipative particle dynamics (mDPD) model recently developed for realistic mesoscale multiphase flow simulations is rigorously parameterized, calibrated, and applied for elucidating the flow mechanisms of hydrocarbon liquids (i.e., heptane in this work) in amorphous silica cylindrical nanochannels with inner diameters ranging from 4.5 to 22.5 nm. The simulation results suggest the presence of a strong threshold of pressure gradient under which heptane cannot be driven to flow. The threshold for the 4.5 nm diameter pore is 10 to 100 times as high as for the 9–22.5 nm diameter pore, highlighting a remarkable nanoconfinement effect. Fluid viscosity is found to exhibit a shear-thinning phenomenon with intensity to weaken with increasing channel diameter — a phenomenon not observed in nanochannel flow of liquid water and gas in literature. Most remarkably, the radial profiles of average longitudinal flow velocity fitted by the modified Hagen-Poiseuille equation showed a negative slip length (–2.5% to –0.5% relative to the diameter) and a reduction of apparent permeability by 16% to 23%. Furthermore, this finding suggests silica nanochannels tend to deter hydrocarbon flow, a phenomenon that is opposed to the flow enhancement reported in most of the prior nanochannel flow studies in literature.
In a producing gas hydrate reservoir the effective porosity available for fluid flow constantly changes with dissociation of gas hydrate. Therefore, accurate prediction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) that were developed for conventional oil and gas reservoirs would require empirical parameters to be calibrated at various S h over its range of variation, but such calibrations are precluded because of lack of experimental relative permeability data. This study proposes a new relative permeability model for gas hydrate-bearing media that is a function of maximum capillary pressure, capillary entry pressure, pore size distribution index, residual saturations, hydrate saturation, and four other constants. The three novel features of the proposed model are: (i) requires fitting its six empirical parameters only once using experimental data from any single S h , and the same set of empirical parameters predict relative permeability at all S h , (ii) includes the effect of capillarity, and (iii) includes the effect of pore-size distribution. From practical standpoint, the model can be used to simulate multiphase flow in gas hydrate-bearing sediments where the proposed relative permeability can account for the evolving hydrate saturation. The proposed model is implemented in a numerical simulator and the wall time required to perform simulations using the proposed model is shown to be similar to the time it takes to run same simulations with the B-C model. The proposed model is a step forward towards achieving the goal of physically accurate modeling of multiphase flow for gas hydrate-bearing sediments that accounts for the effect of gas hydrate saturation change on relative permeability.
MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.
The multisegmented wellbore model (MSW) semi-analytically estimates the amount of CO 2 and brine leakage from a leaking legacy well by segmenting it into intervals to simulate site-specific stratigraphic and hydrogeologic properties. The model is a component of the National Risk Assessment Partnership Open-Source Integrated Assessment Model (NRAP-Open-IAM), which was developed to perform risk assessment for geologic CO 2 storage. The new wellbore leakage model, which uses deep learning networks for a caprock segment, was developed to enhance the analytical MSW. The model was trained and validated using a synthetic data set of Subsurface Transport Over Multiple Phases (STOMP) multiphase flow simulations from various geological, well attribute, and operational conditions to ensure its quality. The results demonstrate that the model is more accurate than the existing model in predicting the transport of two-phase fluids (brine and injected CO 2 ) through the well. This report provides a detailed explanation of the model development and quality assurance.
Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.
Computational investigation of the countercurrent flows in an additively manufactured (AM) column packed with triply periodic minimal surface (TPMS) is challenging. Multiphase flow simulations using the volume of fluid (VOF) method can explain the column’s hydrodynamic characteristics, including interfacial area, liquid holdup, pressure drop, flow regime, etc. The interfacial area is a key factor dictating the mass transfer via the absorption process, and thereby the overall efficiency of the column. The impacts of a solvent's physical properties, liquid and gas loads, and contact angle on the hydrodynamics are extensively explored. The solvents include traditional solvent (monoethanolamine [MEA]) used in industry as well as a water-lean solvent (EEMPA) as prospective solvents for carbon capture. As expected, interfacial area and liquid holdup increase with increasing liquid loads (q L ). The EEMPA exhibits higher values for the interfacial area and liquid holdup compared to those for the MEA. The dry and wet pressure drops per unit length in the column at different gas loads (F G ) are calculated and further compared with the empirical correlations for traditional packings. At low gas loads, i.e., in the preloading regime, the predicted values of the wet and dry pressure drops match well with the corresponding values calculated from available correlations. The gas load has a marginal impact on the interfacial area at its lower value, but the higher value of F G (>2.18 $\sqrt{Pa}$) causes flooding in the column. The effect of varying contact angles on the interfacial area and liquid holdup in the preloading regime is also studied. Both interfacial area and liquid holdup decrease with the increase in the value of the contact angle. Altogether, the effect of the contact angle on interfacial area is more pronounced than other parameters influencing the interfacial area.
This report describes the development and testing of three component models for NRAP-Open-IAM, the National Risk Assessment Partnership’s open-source integrated assessment model. The FutureGen2 Lookup Table Reservoir component model is based on interpolation of data from a set of lookup tables. The lookup tables contain pressures and saturations predicted by multiphase flow simulations performed with a reservoir simulator. The FutureGen2 Above Zone Monitoring Interval (AZMI) component is a surrogate model that can be used to estimate the impact that carbon dioxide (CO 2 ) and brine leaks from the CO 2 storage reservoir at the FutureGen 2.0 site might have had on overlying aquifers or monitoring units were a leak to occur. The model estimates the size of “impact plumes” according to five metrics: pH, Total Dissolved Solids (TDS), pressure, dissolved CO 2 and temperature. The FutureGen2 Aquifer component is similar, but is limited to four metrics: pH, Total Dissolved Solids (TDS), pressure, and dissolved CO 2 . The input parameters for each model are the same, but the Aquifer component is applicable to depths between 100 m and 700 m and the AZMI component is applicable from depths between 700 m and 1050 m.
The Generic Aquifer Model calculates the concentrations of dissolved salt and dissolved CO 2 surrounding a leaking legacy well. The Generic Aquifer model can also estimate the size of an “impact plume” where concentration changes exceed user-specified thresholds. The model is a component of NRAP-Open-IAM, an open-source Integrated Assessment Model (IAM) developed by the National Risk Assessment Partnership (NRAP) to perform risk assessment for geologic CO 2 storage. The input parameters were selected to cover a wide range of groundwater aquifers and leakage rates. The generic aquifer model was developed using a generative adversarial deep learning network, trained using a large synthetic dataset of STOMP multiphase flow simulations. The deep learning model predictions of dissolved salt and dissolved CO 2 in the aquifer compare well to the original STOMP simulation results. The extent of aquifer impacted by leaking CO 2 or brine is calculated using a user-defined mass fraction threshold. The aquifer impact volumes calculated based on STOMP simulation results compare well to those calculated based on the deep learning model. In a provided python script, gridded observation results from the generic aquifer component of NRAP-Open-IAM are converted to HDF5 format files for monitoring design with the DREAM code.
In the preceding project year two, we refined three DBC formulations from a selection of over 50 different chemistries. The optimization study primarily encompassed testing for (i) reversibility extent, (ii) performance in the presence of chemical additives, (iii) adhesion and friction behavior during displacement in wellbores and pipelines, (iv) corrosion protection performance, and (v) injection performance with model fracture systems at the laboratory scale. Highly promising results obtained from all these tests signify the significant potential of DBCs in enhancing hydrocarbon recovery from unconventional reservoirs. The primary activities in the third project year included publishing experimental findings across multiple articles and conducting outreach initiatives. Throughout the year, we undertook tasks such as replicating experimental results, further optimizing various formulations and their associated experimental sets, and conducting additional tests to address missing components based on reviewer feedback and suggestions. We also explored the surfactant and friction-reduction aspects of selected formulations through drag reduction tests. In addition, we constructed an improved fracturing performance setup and performed flow injection tests. The specific DBC formulations focused on during this project period were A8/B1, A12/B5, and A10/B12. We also obtained results for additional DBC formulations and a select few commercial fracturing fluids for the purpose of comparison. Within the project's scope, we aim to enhance the experimental findings with the development of various models. The first two years focused on two key aspects: (i) the creation of a high-fidelity hydraulic fracturing model for non-Newtonian fluids to gain insights into the implementation of DBC fluids in fracking environments, and (ii) the development of a multiphase flow simulator for estimating total production, fluid saturation in the reservoir, and the creation of a fracture propagation model and kinetic Monte Carlo (kMC) models for diverse applications. In the third year, we delved into the fundamental nanostructural properties of DBCs, exploring aspects such as material chemistry, pH tunability, and control of DBC formation and stability. Subsequently, in the extension year, we conducted a systematic investigation of various building blocks containing primary, secondary, and tertiary amine functional groups to understand their impact on rheological and viscoelastic properties. Furthermore, we explored a Dissipative Particle Dynamics (DPD) model to simulate self-assembly processes with precision, creating a high-fidelity representation of relevant nanostructures. The tasks performed this year with the significant results obtained have been discussed in Section 2.
Coupled thermal, hydraulic, and mechanical processes in porous materials play important roles in several energy and environmental technologies. The Darcy-Brinkman-Biot (DBB) framework has proven effective in modeling multiphase fluid flow in deformable porous solids across both pore and Darcy scales, including in systems where fractures coexist with a porous matrix. In this study, we extend the DBB framework, originally designed for isothermal conditions, to address non-isothermal problems by incorporating an energy conservation equation. The resulting solver, hybridBiotThermalInterFoam, enables simulations of coupled multiphase fluid flow, heat transfer, and solid deformation in hybrid-scale systems containing both solid-free regions and ductile porous domains. The new solver is validated through comparisons with analytical solutions and, also, against established heat transfer solvers chtMultiRegionFoam and compressibleInterFoam. Further, a series of 2D and 3D case studies, including two-phase heat transfer in solid-free, static, or deformable porous media, highlights the solver's capacity to simulate complex flow dynamics and heat transport in systems involving high mobility ratios, viscous fingering, and fracture propagation. Our results establish the feasibility of incorporating thermal effects in simulations of a wide variety of energy geotechnics and environmental applications, including enhanced hydrocarbon recovery, soil remediation, and enhanced geothermal energy systems.
In this work we present a comprehensive investigation of state-of-the-art closure models employed to represent interphase momentum, thermal, and work exchange between the gas and particulate phases for Euler–Lagrange (EL) simulations in shock-driven flows. A complete list of closures for the force, torque, heat transfer, and work exchange models is provided. In particular, the present work includes a stochastic closure for the particle-to-particle variation in the quasi-steady force and a deterministic closure for particle-to-particle variation in the added mass force in an EL framework. These variations arise due to the presence of neighboring particles and particle–particle interactions. To investigate the importance of each closure term, we carry out fully three-dimensional simulations for a planar shock propagating over a random bed of inert particles. The primary goal is to evaluate the role of each closure term on the gas dynamic features (such as transmitted and reflected shock locations) and particle curtain features (such as upstream and downstream curtain locations). To this end, thirteen cases are considered, with each case progressively including a closure model with the goal to identify and quantify its contribution to the simulated dynamics. We show that the volume fraction dependence of the mean force models plays an important role in generating wave-like instabilities that lead to concentration bands. In addition, fluctuations in quasi-steady and added mass forces primarily decrease the internal instabilities that tend to enhance local volume fraction variations. Particle rotation is primarily due to inter-particle collisions, is generally weak, and does not play an important role in the translational dynamics for the present configuration. Inter-phase heat transfer has a strong effect on gas phase temperature, slows down the transmitted and reflected shocks, and decreases the width of the curtain. Furthermore, the absence of a work-coupling model fails to conserve the total energy, greatly under-predicts the gas temperature which in turn affects the particle dynamics.
Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.
In fractured natural formations, the equations governing fluid flow and geomechanics are strongly coupled. Hydrodynamical properties depend on the mechanical configuration, and they are therefore difficult to accurately resolve using uncoupled methods. In recent years, significant research has focused on discretization strategies for these coupled systems, particularly in the presence of complicated fracture network geometries. In this work, we explore a finite-volume discretization for the multiphase flow equations coupled with a finite-element scheme for the mechanical equations. Fractures are treated as lower dimensional surfaces embedded in a background grid. Interactions are captured using the embedded discrete fracture model (EDFM) and the embedded finite element method (EFEM) for the flow and the mechanics, respectively. This nonconforming approach significantly alleviates meshing challenges. EDFM considers fractures as lower dimension finite volumes that exchange fluxes with the rock matrix cells. The EFEM method provides, instead, a local enrichment of the finite-element space inside each matrix cell cut by a fracture element. Both the use of piecewise constant and piecewise linear enrichments are investigated. They are also compared to an extended finite element approach. One key advantage of EFEM is the element-based nature of the enrichment, which reduces the geometric complexity of the implementation and leads to linear systems with advantageous properties. Synthetic numerical tests are presented to study the convergence and accuracy of the proposed method. It is also applied to a realistic scenario, involving a heterogeneous reservoir with a complex fracture distribution, to demonstrate its relevance for field applications.