Multiscale Transport Research Articles

Scalable simulation of coupled adsorption and transport of methane in confined complex porous media with density preconditioning

The growing significance of shales and tight formations in the transition to less carbon-intensive and clean energy drives the research endeavor to understand the physics of gas flow within these systems. However, shales are composed of massively heterogeneous physical and chemical features. Most nano-sized pores connect to millimeter-scale fractures, leading to multiscale transport. These nano-scale pore throats demonstrate non-classical flow behavior, such as non-negligible slip velocities and adsorbed gas layers at the boundary. As a result, classical computational fluid dynamics models do not capture the physics. In this work, we develop a coupling scheme for the multiple-relaxation-time (MRT) lattice Boltzmann (LB) method that integrates the Peng-Robinson equation of state into a pseudo-potential interaction model to capture the physics of methane flow in irregular networks of channels that represent nano-scale porous media. We use atomistic simulations to calibrate and validate our model in slit nano-channels. We propose a preconditioning scheme to initialize the coupled transport and adsorption simulation of methane in complex porous media. The results of this implementation of LB agree with Direct Simulation Monte Carlo (DSMC) and Molecular Dynamics (MD) simulations. We then scale up the LB implementation through vectorization and indirect addressing. We parallelize it using Message Passing Interface (MPI) and OpenMP frameworks to simulate transport and adsorption in complex media with a million lattices. We analyze the differences between coupled and transport-only simulations in two case studies and show that considering phase behavior, i.e., adsorption, can significantly change the flow behavior. This work constitutes an important step towards bridging the gap between molecular flow and system-scale behavior of complex disordered porous media.

A multi-physics coupled multi-scale transport model for CO2 sequestration and enhanced recovery in shale formation with fractal fracture networks

CO2 sequestration and enhanced gas recovery (CS-EGR) of shale formation involve complex multi-physics couplings, which are still not precisely captured by recent models. In this regard, a modified model fully considering the fractal characterization of fracture networks and multi-physics coupled transport behaviors in multi-scale shale formation is established. The fractal fracture networks are depicted based on the L-system theory with microseismic events (MSE), and embedded into the numerical modeling by an automatic fitting algorithm. As a development from the traditional transport model, the formulas among the pressure field, thermal field, and the molecular diffusion of binary gases are constructed based on the Chapman-Enskog theory. The effect of tortuosity and surface diffusion on gas transport efficiency can reach up to 5 %. The morphology parameters of induced fractures affect gas migration mainly by changing the size of SRV and the space between adjacent induced fractures, with their influence on CH4 production potentially as high as 20 %. Meanwhile, there are improved CH4 production, energy efficiency, and CO2 storage amounts, when the injection parameters are optimized. By optimizing the injection conditions (temperature, pressure), CH4 production can be increased by about 8 %.

A Review of Macroscopic Modeling for Shale Gas Production: Gas Flow Mechanisms, Multiscale Transport, and Solution Techniques

The boost of shale gas production in the last decade has reformed worldwide energy structure. The macroscale modeling of shale gas production becomes particularly important as the economic development of such resources relies on the deployment of expensive hydraulic fracturing and the reasonable planning of well schedules. A flood of literature was therefore published focused on accurately and efficiently simulating the production performance of shale gas and better accounting for the various geological features or flow mechanisms that control shale gas transport. In this regard, this paper presents a holistic review of the macroscopic modeling of gas transport in shale. The review is carried out from three important points of view, which are the modeling of the gas flow mechanisms, the representation of multiscale transport, and solution techniques for the mathematical models. Firstly, the importance of gas storage and flow mechanisms in shale is discussed, and the various theoretical models used to characterize these effects in the continuum scale are introduced. Then, based on the intricate pore structure and various pore types of shale gas reservoirs, this review summarizes the multiple-porosity models in the literature to represent multiscale gas transport, and discusses the applicability of each model. Finally, the numerical and analytical/semi-analytical approaches used to solve the macroscopic mathematical model governing shale gas production are reviewed, with a focus on the treatment of the complex fracture network formed after multistage hydraulic fracturing.

A hybrid physics-informed data-driven neural network for CO2 storage in depleted shale reservoirs

To reduce CO2 emissions in response to global climate change, shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes. However, most current CO2 sequestration models do not adequately consider multiple transport mechanisms. Moreover, the evaluation of CO2 storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making. In this paper, an integrated model involving gas diffusion, adsorption, dissolution, slip flow, and Darcy flow is proposed to accurately characterize CO2 storage in depleted shale reservoirs, supporting the establishment of a training database. On this basis, a hybrid physics-informed data-driven neural network (HPDNN) is developed as a deep learning surrogate for prediction and inversion. By incorporating multiple sources of scientific knowledge, the HPDNN can be configured with limited simulation resources, significantly accelerating the forward and inversion processes. Furthermore, the HPDNN can more intelligently predict injection performance, precisely perform reservoir parameter inversion, and reasonably evaluate the CO2 storage capacity under complicated scenarios. The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources. This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO2 storage projects in depleted shale reservoirs.

A new multi-scale pore transport capacity model considering elliptical shape and retention effect of unequal-thickness water films

Most of current permeability evaluation models for shale gas reservoirs ignore the influence of imbibition during fracturing, moreover, several of the considerations are different from real conditions, for example, shale pore shape is circular and water film is with equal thickness, which is convenient for calculation but adverse to evaluation accuracy. In the study, shale pore shapes are closer to ellipse by statistically analyzing the scanning electron microscopy results, the thickness of water film is not equal on elliptical pore surface but related to the curvature. Based on these new insights, a quantitative model for determining the unequal thickness of water film was established, and then a new multi-scale elliptical pore permeability model, considering non-equal-thickness water film, wall roughness, stress sensibility, and retention effect, was established based on the multi-scale flow weighting theory. The prediction results of this permeability model are similar to the test results, and are more accurate than that of the previous models. Finally, factors analysis showed that the effect of roughness on permeability was small, but the negative impact of water film and stress sensitivity on permeability was significant.

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process.

Metal-based additive manufacturing (MAM) method with high freedom and special fabricate technology has presented great universality in the aerospace and biomedicine field. However, a wide range of process parameters in the MAM method challenge the experimental study on the formation and evolution of defects. The numerical simulation presents its excellent accuracy and economy in predicting the evolution of multiphysics phenomena and was hence widely applied. In the current review, the available MAM methods with the fundamental phenomena were reviewed. Based on scales, numerical approaches divided into three categories were discussed and focused on their main prediction objectives and strengths or weaknesses of all the scales. To display the prediction results closer to real physical phenomena, advanced multiscale models coupled with various single-scale models are provided. The high prediction accuracy and computational efficiency enable better parameter control and defect avoidance. As a supplement and development to the physical-driven model, the data-driven model provides a new perspective on MAM methods. Based on the database generated by the physical-driven model and experiment, the data-driven models without calibration of input parameters are shown. In addition, this review discussed the development direction of numerical simulation, aiming to provide a reference for technical research in this field.

Properties and Model of a Lacustrine Shale Oil Reservoir: A Case Study of the Upper Fourth Member and Lower Third Member of the Shahejie Formation in Dongying Sag and Zhanhua Sag, Jiyang Depression

By combining thermal simulation experiments with core data, thin sections, scanning electron microscopy (SEM), source rock, and high-pressure mercury and gas adsorption analysis, this paper evaluates the properties and models of shale reservoirs in the upper fourth member and the lower third member of the Shahejie Formation in Dongying Sag and Zhanhua Sag. The results show that the oil shale is a high-quality source rock with petroleum generation potential. Inorganic mineral pores, organic pores, and fractures have developed with petroleum storage capacity. Clay and organic-rich soft shale lithofacies are interbedded with carbonate-rich hard shale lithofacies to form sandwich-type source–reservoir–caprock assemblages with internal sealing properties. Bitumen occurs mostly in the free state, and to a lesser extent is adsorbed, and shows flow characteristics. The migration resistance of the bitumen is displacement pressure, including capillary force and adsorption resistance, and the main force is pore fluid pressure. The migration modes of the bitumen include both subcritical flow and instantaneous flow, which are controlled by pore fluid pressure, displacement pressure, and the rock’s fracture strength. Owing to the multi-scale characteristics of the shale reservoir space, a vein-type model of a multi-scale progressive transport network is developed that obeys Murray’s law with a dominant migration pathway in the shale reservoir. The shale oil reservoir is a special lithologic reservoir controlled by the sedimentary environment and has self-generation, self-storage, and self-sealing characteristics with developed pore fluid overpressure and a multi-scale transport network.

Numerical evaluation of photosensitive tracers as a strategy for separating surface and subsurface transient storage in streams

We numerically evaluated photosensitive tracers as a potential strategy for separating the effects of surface and hyporheic storage zones (SSZs and HSZs, respectively) on stream corridor transport. Correctly separating HSZ and SSZ effects is critical to estimating the hydro-biogeochemical function of a stream because HSZs and SSZs expose solutes to significantly different biogeochemical conditions, like sunlight exposure, microbial processes, and oxygen availability. Our numerical experiments used a multiscale river-corridor transport model implemented in the ATS code, which accommodates multiple storage zones with distinct travel time distributions and biogeochemical reactions. For parameter inferences, we used Bayesian inverse modeling. We found that breakthrough curves for photo-decaying tracers from day and night injection can delineate surface and hyporheic transient storage contributions, but only when interpreted jointly through a two-storage zone model. Numerical experiments that used only daytime injection or interpreted breakthrough curves with a single storage zone model yielded good fit to breakthrough curves, but parameter estimates were biased and controlling processes misattributed, examples of good model fits for the wrong reasons. Using those biased parameter estimates in reactive transport simulations resulted in significantly different projections of denitrification, which underscores the potential for stream function to be mischaracterized if tracer tests are interpreted through an inappropriately simplified model for transient storage. More generally, this study highlights the role of modeling in evaluating the experimental design and identifying the potential of system mischaracterization and its implications.

The dynamic thermophysical properties evolution and multi-scale heat transport mechanisms of 2.5D C/SiC composite under high-temperature air oxidation environment

The variations in thermophysical properties and dynamic heat transfer mechanism of 2.5D C/SiC ceramic matrix composites (2.5D C/SiC-CMC) under a high-temperature air oxidation environment are investigated by experiments and numerical simulations. A new multi-scale thermal analysis model of 2.5D C/SiC-CMC under high-temperature oxidation is proposed to predict its equivalent thermal conductivity and analysis its multi-scale heat transport mechanisms. The multi-scale thermal analysis model is developed by blending the multi-scale asymptotic thermal analysis method and oxidation kinetics theory, which includes the micro-scale model and mesoscale model. The micro-scale model simulates the oxidation process of the carbon fibers and PyC interface through oxidation kinetics, which predicts the varying equivalent thermal conductivity of yarns with oxidation. The mesoscale model is constructed based on the experimental statistics which reflect the multi-scale structure within the 2.5D C/SiC-CMC with oxidation. The mesoscale model introduces oxidation characteristics by considering dynamic variations in yarn thermal properties. The dynamic thermophysical properties of the 2.5D C/SiC-CMC with oxidation are tested in the experimental study, which verifies that the multi-scale thermal analysis model has highlighted prediction accuracy. The results show that high-temperature oxidation causes significant variations in the dynamic thermal behavior of 2.5D C/SiC-CMC which is critical for the practical thermal protection design of 2.5D C/SiC-CMC hot components in engineering applications.

Probing Surface Chemistry Effects on Effective Diffusion Coefficients in Hierarchically Porous Media Through Multiscale Simulations

AbstractMultiscale diffusion simulations in a realistic macro‐mesoporosity model of a silica‐based chromatographic bed are performed to study the effect of relevant surface chemistry parameters, namely length and ligand density of surface‐tethered alkyl chains, on effective mesopore and bed diffusion coefficients. Efficient linker schemes enable integration of interfacial dynamics information obtained from molecular dynamics simulations at the single‐mesopore level into the hierarchical porosity and multiscale transport model.

A multiscale model for solute transport in stream corridors with unsteady flow

The transport of solutes in stream corridors is an important process for riverine biogeochemistry and ecosystem services. This process is largely controlled by bulk movement in the stream channel and mass exchange with the hyporheic zone. However, modeling solute transport in stream corridors under dynamic hydrological conditions remains a scientific challenge. Here, we extend a steady-state Lagrangian multiscale river-corridor transport model to accommodate unsteady flow. The extended numerical model was evaluated with observations of conservative tracer at a headwater mountain stream using Markov-Chain Monte Carlo method to estimate model parameters and quantify uncertainty. Parameter estimation was exposed to a range of unsteady flow conditions through joint fitting of tracer data observed at different intervals of the base flow recession. This yielded a robust scaling of hyporheic parameters with time-varying streamflow which is evident from the good fit between modeled outputs and observations. The proposed approach enables Lagrangian-based modeling of solute transport under dynamic flow regimes, e.g., storm-affected flow where discharge inevitably changes over time. It also paves way for extending Lagrangian-based transport models to catchment and larger spatial scales.

Multiscale Transport Characteristics of Coalbed Methane in Abandoned Mines and Its Applications in Gas Recovery

Multiscale Transport Characteristics of Coalbed Methane in Abandoned Mines and Its Applications in Gas Recovery

Multiscale steady discrete unified gas kinetic scheme with macroscopic coarse mesh acceleration using preconditioned Krylov subspace method for multigroup neutron Boltzmann transport equation.

A multiscale steady discrete unified gas kinetic scheme with macroscopic coarse mesh acceleration [accelerated steady discrete unified gas kinetic scheme (SDUGKS)] is proposed to improve the convergence of the original SDUGKS for an optically thick system in solving the multigroup neutron Boltzmann transport equation (NBTE) to analyze the distribution of fission energy in the reactor core. In the accelerated SDUGKS, by solving the coarse mesh macroscopic governing equations (MGEs) derived from the moment equations of the NBTE, the numerical solutions of the NBTE on fine meshes at the mesoscopic level can be rapidly obtained from the prolongation of the coarse mesh solutions of the MGE. Furthermore, the use of the coarse mesh can greatly reduce the computational variables and improve the computational efficiency of the MGE. The biconjugate gradient stabilized Krylov subspace method with the modified incomplete LU preconditioner and the lower-upper symmetric-Gauss-Seidel sweeping method are implemented to solve the discrete systems of the macroscopic coarse mesh acceleration model and mesoscopic SDUGKS to further improve the numerical efficiency. Numerical solutions validate good numerical accuracy and high acceleration efficiency of the proposed accelerated SDUGKS for the complicated multiscale neutron transport problems.

Experimental Evaluation of Working Fluid Damage to Gas Transport in a High-Rank Coalbed Methane Reservoir in the Qinshui Basin, China.

Formation damage induced by the injected working fluid runs through the whole life cycle of coalbed methane (CBM) extraction and ultimately reduces the production of CBM wells. The conventional method uses permeability as a parameter to evaluate the formation damage severity to coal by working fluids containing solids. However, less attention has been attracted to the formation damage of the pure liquid phase of the working fluid on the multiscale gas transport process of CBM. Therefore, we present a multiscale working fluid filtrate damage evaluation method considering the desorption, diffusion, and seepage and use it to evaluate high-rank coal in the Qinshui Basin of China. The results show that pure liquids with different pH values and salinities significantly damage the desorption-diffusion and seepage ability of CBM. The damage rates of alkaline fluid, hydrochloric acid fluid, and clear water on the methane desorption capacity of coal are 63.64, 17.63, and 24.34%, respectively, while those on the permeability of coal are 29.88, 42.38, and 46.66%, respectively. The formation damage severity in the seepage process is higher than that in the desorption-diffusion process, which proves the necessity of multiscale working fluid damage evaluation on CBM. Effective channel reduction and resistance increase in gas transport are the mechanisms of working fluid filtrate-induced formation damage, which are caused by water blocking, sensitive mineral swelling and clogging, and strengthened stress sensitivity. In addition to controlling the solid damage of the working fluid, reducing the invasion of the working fluid filtrate and maintaining its compatibility with the coal and formation fluids are even more important to protect the coal reservoir.

Multiscale Architectured Membranes, Electrodes, and Transport Layers for Next-Generation Polymer Electrolyte Membrane Fuel Cells.

Over the past few decades, considerable advances have been achieved in polymer electrolyte membrane fuel cells (PEMFCs) based on the development of material technology. Recently, an emerging multiscale architecturing technology covering nanometer, micrometer, and millimeter scales has been regarded as an alternative strategy to overcome the hindrance to achieving high-performance and reliable PEMFCs. This review summarizes the recent progress in the key components of PEMFCs based on a novel architecture strategy. In the first section, diverse architectural methods for patterning the membrane surface with random, single-scale, and multiscale structures as well as their efficacy for improving catalyst utilization, charge transport, and water management are discussed. In the subsequent section, the electrode structures designed with 1D and 3D multiscale structures to enable low Pt usage, improve oxygen transport, and achieve high electrode durability are elucidated. Finally, recent advances in the architectured transport layer for improving mass transportation including pore gradient, perforation, and patterned wettability for gas diffusion layer and 3D structured/engineered flow fields are described.

Promotion of Wound Healing Using Nanoporous Silk Fibroin Sponges.

Wound dressings are important for wound repair. The morphology of the biomaterials used in these dressings, and in particular, the pore structure affects tissue regeneration by facilitating attachment and proliferation of cells due to the hierarchical multiscale, water absorbance, and nutrient transport. In the present study, silk fibroin (SF) sponges with walls containing nanopores (SFNS) were prepared from SF nanoparticles generated during the autoclaving of SF solutions, followed by leaching the SF nanoparticles from the freeze-dried sponges of SF. The nano/microporous structure, biofluid absorbance, and porosity of the SF sponges with and without nanopores were characterized. In vitro cell proliferation, in vivo biocompatibility, and wound healing were evaluated with the sponges. The results demonstrated that SFNS had significantly increased porosity and water permeability, as well as cell attachment and proliferation when compared with SF sponges without the nanopores (SFS). Wound dressings were assessed in a rat skin wound model, and SFNS was superior to SFS in accelerating wound healing, supported by vascularization, deposition of collagen, and increased epidermal thickness over 21 days. Hence, such a dressing material with a hierarchical multiscale pore structure could promote cell migration, vascularization, and tissue regeneration independently without adding any growth factor, which would offer a new strategy to design and engineer better-performed wound dressing.

Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm2) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm2 with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities.

A two-phase type-curve method with multiscale fluid transport mechanisms in hydraulically fractured shale reservoirs

The quantitative understanding of hydraulic fracture (HF) properties guides accurate production forecasts and reserve estimation. Type curve is a powerful technique to characterize HF and reservoir properties from flowback and long-term production data. However, two-phase flow of water and hydrocarbon after an HF stimulation together with the complex transport mechanisms in shale nanopores exacerbate the nonlinearity of the transport equation, causing errors in type-curve analysis. Accordingly, we propose a new two-phase type-curve method to estimate HF properties, such as HF volume and permeability of fracture, through the analysis of flowback data of multi-fractured shale wells. The proposed type curve is based on a semianalytical solution that couples the two-phase flow from the matrix with the flow in HF by incorporating matrix influx, slippage effect, stress dependence, and the spatial variation of fluid properties in inorganic and organic pores. For the first time, multiple fluid transport mechanisms are considered into two-phase type-curve analysis for shale reservoirs. We analyze the flowback data from a multi-fractured horizontal well in a shale gas reservoir to verify the field application of the proposed method. The results show that the fracture properties calculated by the type-curve method are in good agreement with the long-time production data.

Asymptotic-Preserving Neural Networks for Multiscale Time-Dependent Linear Transport Equations

In this paper we develop a neural network for the numerical simulation of time-dependent linear transport equations with diffusive scaling and uncertainties. The goal of the network is to resolve the computational challenges of curse-of-dimensionality and multiple scales of the problem. We first show that a standard Physics-Informed Neural Network (PINN) fails to capture the multiscale nature of the problem, hence justifies the need to use Asymptotic-Preserving Neural Networks (APNNs). We show that not all classical AP formulations are directly fit for the neural network approach. We construct a micro-macro decomposition based neural network, and also build in a mass conservation mechanism into the loss function, in order to capture the dynamic and multiscale nature of the solutions. Numerical examples are used to demonstrate the effectiveness of this APNNs.

Evaluating multiscale and multimodal transport inequalities in Chinese cities with massive open-source path data

Evaluating multiscale and multimodal transport inequalities in Chinese cities with massive open-source path data