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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Your Clean Graphene is Still Not Clean

Researchers working with thin samples, such as monolayer graphene, are consistently struggling against contamination. Indeed, the problem of hydrocarbon contamination is known from the earliest days of electron microscopy and efforts to reduce this problem are ubiquitous to almost all high-vacuum experiments. Accurate knowledge of the behavior of such contamination is essential for electron beam (e-beam) based atomic fabrication, where it is aspired to select and control matter on an atom-by-atom basis. Here, the vexing question of hydrocarbon contamination on graphene is taken up. Image intensity is used to directly reveal the presence of diffusing hydrocarbons on ostensibly clean graphene. These diffusing hydrocarbons are previously inferred but not directly observed. Surprising dynamic variations of the concentration of these hydrocarbons impels questions about their origin. Here, some possible explanations are presented and some tentative conclusions are drawn. This work updates the conceptual model of “clean graphene” and offers refinements to the description of e-beam induced hydrocarbon deposition.

atomic fabrication↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Testing nonlocal models of electron thermal conduction for magnetic and inertial confinement fusion applications

Three models for nonlocal electron thermal transport are here compared against Vlasov-Fokker-Planck (VFP) codes to assess their accuracy in situations relevant to both inertial fusion hohlraums and tokamak scrape-off layers. The models tested are (i) a moment-based approach using an eigenvector integral closure (EIC) originally developed by Ji, Held, and Sovinec [Phys. Plasmas 16, 022312 (2009)]; (ii) the non-Fourier Landau-fluid (NFLF) model of Dimits, Joseph, and Umansky [Phys. Plasmas 21, 055907 (2014)]; and (iii) Schurtz, Nicolaï, and Busquet’s [Phys. Plasmas 7, 4238 (2000)] multigroup diffusion model (SNB). We find that while the EIC and NFLF models accurately predict the damping rate of a small-amplitude temperature perturbation (within 10% at moderate collisionalities), they overestimate the peak heat flow by as much as 35% and do not predict preheat in the more relevant case where there is a large temperature difference. The SNB model, however, agrees better with VFP results for the latter problem if care is taken with the definition of the mean free path. Additionally, we present for the first time a comparison of the SNB model against a VFP code for a hohlraum-relevant problem with inhomogeneous ionisation and show that the model overestimates the heat flow in the helium gas-fill by a factor of ∼2 despite predicting the peak heat flux to within 16%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Solidification and grain formation in alloys: a 2D application of the grand-potential-based phase-field approach

Solidification is a significant step in the forming of crystalline structures during various manufacturing and material processing techniques. Solidification characteristics and the microstructures formed during the process dictate the properties and performance of the materials. Hence, understanding how the process conditions relate to various microstructure formations is paramount. In this work, a grand-potential-based multi-phase, multi-component, multi-order-parameter phase-field model is used to demonstrate the solidification of alloys in 2D. This model has several key advantages over other multi-phase models such as it decouples the bulk energy from the interfacial energy, removes the constraints for the phase concentration variable, and prevents spurious third-phase formation at the two phase interfaces. Here, the model is implemented in a finite-element-based phase-field modeling code. The role of various modeling parameters in governing the solidification rate and the shape of the solidified structure is evaluated. It is demonstrated that the process conditions such as temperature gradient, thermal diffusion, cooling rate, etc, influence the solidification characteristics by altering the level of undercooling. Furthermore, the capability of the model to capture directional solidification and polycrystalline structure formation exhibiting various grain shapes is illustrated. In both these cases, the process conditions have been related to the growth rate and associated shape of the dendritic structure. As a result, this work serves as a stepping stone towards resolving the larger problem of understanding the process–structure–property–performance correlation in solidified materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solving disorder in (3D) real space: a comparative study of the three-dimensional difference pair distribution function and atomic resolution holography reconstructions

The quantitative analysis of local ordering principles in disordered crystalline systems has gained much attention over the past few years, as it is often considered crucial for optimizing material functionality. This development has been driven by significant advancements in computational and experimental methods, which have led to the establishment and widespread use of various analytical techniques. In this study, we perform model calculations to compare the effectiveness of atomic resolution holography and three-dimensional difference pair distribution function analysis (3D-ΔPDF). Using Cu 3 Au as a model system, we demonstrate an approach to derive local order parameters quantitatively and show that both techniques are well suited to quantifying chemical short-range order correlations and local bond-distance variations. By evaluating the strengths and limitations of both techniques, we advocate for their combined use to solve complex short-range order problems accurately.

3D-ΔPDF↗
DOE OSTI2025

DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems

We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms. We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores. Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges). By overcoming these limitations, we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges. This two orders-of-magnitude improvement over the previous state-of-the-art is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application’s memory requirements. We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds. We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.

Minutoli, Marco [Pacific Northwest National Labora↗
DOE OSTI2017

Polarized 3He gas circulating technologies for neutron analyzers

We outline our project to develop a circulating polarized helium-3 system for developing of large, quasi-continuously operating neutron analyzers. The project consisted of four areas: 1) Development of robust external cavity narrowed diode laser output with spectral line width < 0.17 nm and power of 2000 W. 2) Development of large glass polarizing cells using cell surface treatments to obtain long relaxation lifetimes. 3) Refinements of the circulation system with an emphasis on gas purification and materials testing. 4) Design/fabrication of a new polarizer system. 5) Preliminary testing of the new polarizer. 1. Developed Robust High-Power Narrowed Laser The optical configuration of the laser was discussed in the proposal and will be reviewed in the body of this report. The external cavity is configured to mutually lock the wavelength of five 10-bar laser stacks. All the logistical milestones were been met and critical subsystems- laser stack manifold and power divider, external laser cavity, and output telescope- were assembled and tested at low power. Each individual bar is narrowed to ~0.05 nm; when combined the laser has a cumulative spectral width of 0.17 nm across the entire beam due to variations of the bars central wavelength by +/- 0.1 nm, which is similar to that of Volume Bragg Grating narrowed laser bars. This configuration eliminates the free-running “pedestal” that occurs in other external cavity diode lasers. The full-scale laser was completed in 2016 and was used in both the older and newer helium polarizers. This laser was operated at 75% power for periods of up to 8 hours. Once installed, the spectrum became slightly broader (~.25 nm) at full power; this is likely due to very slight misalignments that occurred during handling. 2. Developed the processes to create uniform sintered sol-gel coatings. Our work on cell development comprised: 1) Production of large GE180 cells and explore different means of cell preparation, and 2) Development of apply sol-gel coatings to the interior of both borosilicate and aluminosilicate cells. We applied six sol-gel coatings. By modifying the mixture and developing procedures to drain and dry the cell, we produced visually uniform coatings on the interior of the cells. We now have perfected that process as described below in our report. We were able to accelerate the testing of cells using an ex situ method that avoids installing each cell into a polarizer. In the project’s last year, we conducted 38 external tests of 8 different cells. We also installed two sol-gel coated cells in our polarizers. We created cell with long ex situ relaxation lifetimes, one of which exceeded 40 hours. However, when installed in the polarizer the measured lifetime is 8 hours or less. 3. Demonstrated cycling of polarized gas and ex situ cell testing We are now cycling polarized gas from the polarizer to glass vessels and back. This has allowed us, for the first time, to make ex situ T1 measurements of polarizing cells without installing them into the polarizer itself. This has greatly improved our productivity in producing cells and evaluating our cell preparation processes. We continued development of the gas handling system in parallel with fabricating new polarizer. The integrated system was tested by the end of 2016. We now regularly cycle gas into and out of the polarizer. 4. Completed new polarizer infrastructure and control systems. We completed the new polarizer infrastructure in November 2016. The polarizer subsystems are 1) the frame, 2) the oil flow system, 3) the gas handling system, 4) the pressure vessel, with embedded solenoid, 5) cell mounting hardware with heat spreaders, and 6) electrical power and instrumentation. 5. Carried out initial tests of polarizer. We completed initial testing of the polarizer in April and May of 2017. These tests were carried out for periods up to 6 hours with laser power between 750 and 1300 Watts. The laser performed well and the polarization with asymptotic to 45 percent, which was below expectations. This low value resulted from a stationary thermal inversion in the cell that caused most of the laser power to be absorbed near the laser inlet window and deprived the lower portions of the cell of pumping laser light. Possible solutions to this problem include enhanced cooling of the cell near the laser entry and slight detuning of the laser. 6. Ongoing work. Our polarizer development efforts are ongoing to pursue our interest in neutron analyzers, nuclear targets, and providing helium for medical imaging. Current tests in the pipeline include: 1. Testing cooling enhancements to improve laser penetration of spectrally narrow lasers; 2. Testing of a cell with isolation valves that minimizes diffusive contact with gas handling hardware during polarization; 3. Testing of smaller hybrid cells with reduced alkali loading; 4. Producing polarized helium-3 for MRI imaging at the University of Missouri.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Velocity-space Origins of the Pressure–Strain Interaction in Multipopulation Distributions and Its Application to Magnetic Reconnection

A forefront research question is how energy evolves in weakly collisional plasmas for which departures from local thermodynamic equilibrium (LTE) are significant. The standard approach is studying the terms in the non-LTE energy evolution equation derived by taking the second moment of the Boltzmann equation, but the resultant fluid metrics do not retain information about which particles at which velocities drive energy evolution. A widely studied channel for internal energy density evolution is the pressure–strain interaction. Here, we employ the kinetic pressure–strain, a phase-space diagnostic whose velocity-space integral recovers the pressure–strain interaction to disambiguate the contributions to the pressure–strain interaction from disparate particle populations in composite phase-space densities. We develop phase-space analogs of the pressure–strain interaction decompositions to provide the phase-space origins of normal versus sheared flow. We introduce the “kinetic strain-rate” tensor, the phase-space analog of the strain-rate tensor, which we argue is needed to interpret the phase-space origins of the pressure–strain interaction. To demonstrate the utility of these quantities, we investigate them for composite electron distributions near the electron diffusion region in two-dimensional particle-in-cell simulations of antiparallel symmetric magnetic reconnection. We find that the phase-space-based diagnostics isolate the roles of distinct populations. These results contribute to a growing body of work providing new methods for quantifying phase-space energy evolution for a broad array of processes, from magnetic reconnection to collisionless shocks and turbulence, opening new pathways for answering longstanding problems of particle energization in weakly collisional plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2017

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor

Predictions of the total energy deposited into a brain tumor through X-ray irradiation are notoriously error-prone. We investigate how this predictive uncertainty is affected by uncertainty in both the location of the region occupied by a dose-enhancing iodinated contrast agent and the agent's concentration. This is done within the probabilistic framework in which these uncertain parameters are modeled as random variables. We employ the stochastic collocation (SC) method to estimate statistical moments of the deposited energy in terms of statistical moments of the random inputs, and the global sensitivity analysis (GSA) to quantify the relative importance of uncertainty in these parameters on the overall predictive uncertainty. A nonlinear radiation–diffusion equation dramatically magnifies the coefficient of variation of the uncertain parameters, yielding a large coefficient of variation for the predicted energy deposition. This demonstrates that accurate prediction of the energy deposition requires a proper treatment of even small parametric uncertainty. Our analysis also reveals that SC outperforms standard Monte Carlo, but its relative efficiency decreases as the number of uncertain parameters increases from one to three. A robust GSA ameliorates this problem by reducing this number.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

How nanoscale protein interactions determine the mesoscale dynamic organisation of bacterial outer membrane proteins

The spatiotemporal organisation of membranes is often characterised by the formation of large protein clusters. In Escherichia coli, outer membrane protein (OMP) clustering leads to OMP islands, the formation of which underpins OMP turnover and drives organisation across the cell envelope. Modelling how OMP islands form in order to understand their origin and outer membrane behaviour has been confounded by the inherent difficulties of simulating large numbers of OMPs over meaningful timescales. Here, we overcome these problems by training a mesoscale model incorporating thousands of OMPs on coarse-grained molecular dynamics simulations. We achieve simulations over timescales that allow direct comparison to experimental data of OMP behaviour. Here, we show that specific interaction surfaces between OMPs are key to the formation of OMP clusters, that OMP clusters present a mesh of moving barriers that confine newly inserted proteins within islands, and that mesoscale simulations recapitulate the restricted diffusion characteristics of OMPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Collaborative Project: An Integrated Parameterization of Boundary Layer and Convective Mixing: The Eddy-Diffusivity/Mass-Flux (EDMF) Approach

The key goal of this project was to reduce cloud modeling deficiencies and biases by developing and implementing a new unified boundary layer and convection parameterization based on the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) approach. This is a turbulence and convection parameterization that can be considered fully unified, in the sense that it is able to represent convective processes from boundary layer convection to deep moist convection with one single parameterization. This project will ultimately lead to more accurate climate predictions, which have a significant societal impact. In general, turbulence and convection in the atmosphere are at the core of key climate prediction problems. For example: i) to reduce uncertainties in climate projections, it is essential to improve predictions of cloud feedbacks (how clouds respond to, and influence, climate change), which are controlled by the interactions between a turbulent flow with water phase transitions and radiation; (ii) to improve extreme weather prediction under climate change, it is essential to improve our understanding of how moist convection responds to a warmer world. A fully unified boundary layer and convection parameterization, such as EDMF, will help better represent and predict boundary layer, cloud and convection processes in the atmosphere during present and future climate. The following are the specific tasks that were completed and addressed the project’s objectives: i) Implementation and evaluation of the multi-plume EDMF parameterization into the Single Column Model (SCM) version of CESM; ii) Development of a fully unified multi-plume (shallow to deep convection) EDMF parameterization – that is also scale-aware; iii) Implementation and evaluation of the moist multi-plume EDMF scheme in the global version of WRF; iv) Extension and evaluation of the LES model to deep moist convection regimes; v) Implementation of EDMF in the SCM version of the LBL dynamical core.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Impurity transport in fractal media in the presence of a degrading diffusion barrier

We have analyzed the transport regimes and the asymptotic forms of the impurity concentration in a randomly inhomogeneous fractal medium in the case when an impurity source is surrounded by a weakly permeable degrading barrier. The systematization of transport regimes depends on the relation between the time t{sub 0} of emergence of impurity from the barrier and time t{sub *} corresponding to the beginning of degradation. For t{sub 0} < t{sub *}, degradation processes are immaterial. In the opposite situation, when t{sub 0} > t{sub *}, the results on time intervals t < t{sub *} can be formally reduced to the problem with a stationary barrier. The characteristics of regimes with t{sub *} < t < t{sub 0} depend on the scenario of barrier degradation. For an exponentially fast scenario, the interval t{sub *} < t < t{sub 0} is very narrow, and the transport regime occurring over time intervals t < t{sub *} passes almost jumpwise to the regime of the problem without a barrier. In the slow power-law scenario, the transport over long time interval t{sub *} < t < t{sub 0} occurs in a new regime, which is faster as compared to the problem with a stationary barrier, but slower than in the problem without a barrier. The asymptotic form of the concentration at large distances from the source over time intervals t < t{sub 0} has two steps, while for t > t{sub 0}, it has only one step. The more remote step for t < t{sub 0} and the single step for t > t{sub 0} coincide with the asymptotic form in the problem without a barrier.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Slip velocity boundary conditions for the lattice Boltzmann modeling of microchannel flows

Abstract Slip flows in ducts are important in numerous engineering applications, most notably in microchannel flows. Compared to the standard no‐slip Dirichlet condition, the case of slip formulates as a Robin‐type condition for the fluid tangential velocity. Such an increase in mathematical complexity is accompanied by a more challenging numerical transcription. The present work concerns with this topic, addressing the modeling of the slip velocity boundary condition in the lattice Boltzmann method (LBM) applied to steady slow viscous flows inside ducts of nontrivial shapes. As novelty, we extend the newly revised local second‐order boundary (LSOB) Dirichlet fluid flow method [ Philos. Trans. R. Soc. A 378, 20190404 (2020)] to implement the slip velocity condition within the two‐relaxation‐time (TRT) framework. The LSOB follows an in‐node philosophy where its operation principle seeks to explicitly reconstruct the unknown boundary populations in the form of a third‐order accurate Chapman–Enskog expansion, where the wall slip condition is built‐in as a normal Taylor‐type condition. The key point of this approach is that the required first‐ and second‐order momentum derivatives, rather than computed through nonlocal finite difference approximations, are locally determined through a simple local linear algebra procedure, whose formulation is particularly aided by the TRT symmetry argument. To express the obtained derivatives, two approaches are considered, called and , which operate with node and wall variables, respectively. These two formulations are developed to prescribe the physical slip condition over plane and curved walls, including the corners. Their consistency and accuracy characteristics are examined against alternative linkwise strategies to impose the wall slip velocity, such as the kinetic‐based diffusive bounce‐back scheme, the central linear interpolation slip scheme, and the multireflection slip scheme. The several slip schemes are tested over different 3D microchannel configurations, with walls not conforming with the LBM uniform mesh. Numerical tests confirm the advanced accuracy characteristics of the proposed LSOB slip boundary scheme, revealing the added challenge of the wall slip modeling, and that parabolic accuracy is a necessary requirement to reach second‐order accuracy within this problem class.

Silva, Goncalo↗
DOE OSTI2020

Correlation Function Approach for Diffusion in Confined Geometries

This paper describes a formalism for extracting spatially varying transport coefficients from simulations of a molecular fluid in a nano channel. This approach is applied to self-diffusion of a Lennard-Jones fluid confined between two parallel surfaces. A numerical grid is laid over the domain confining the fluid, and fluid properties are projected onto the grid cells. The time correlation functions between properties in different grid cells are calculated and can be used as the basis for a fitting procedure for extracting spatially varying diffusion coefficients from the simulation. Results for the Lennard-Jones system show that transport behavior varies sharply near the liquid-solid boundary and that the changes depend on the details of the liquid-solid interaction. A quantitative difference between the reduced and detailed models is discussed. It is found that the difference could be associated with assumptions about the form of the transport equations at molecular scales in lieu of problems with the method itself. The study suggests that this approach to fitting molecular simulations to continuum equations may guide the development of appropriate coarse-grained equations to model transport phenomena at nanometer scales.

Nanoscale flow, Transport, molecular simulation↗
DOE OSTI2026

Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)

This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Water Saturation Relations and Their Diffusion-Limited Equilibration in Gas Shale: Implications for Gas Flow in Unconventional Reservoirs

Large volumes of water are used for hydraulic fracturing of low permeability shale reservoirs to stimulate gas production, with most of the water remaining unrecovered and distributed in a poorly understood manner within stimulated regions. Because water partitioning into shale pores controls gas release, we measured the water saturation dependence on relative humidity (rh) and capillary pressure (P c ) for imbibition (adsorption) as well as drainage (desorption) on samples of Woodford Shale. Experiments and modeling of water vapor adsorption into shale laminae at rh = 0.31 demonstrated that long times are needed to characterize equilibrium in larger (5 mm thick) pieces of shales, and yielded effective diffusion coefficients from 9 × 10 -9 to 3 × 10 -8 m 2 s -1 , similar in magnitude to the literature values for typical low porosity and low permeability rocks. Most of the experiments, conducted at 50°C on crushed shale grains in order to facilitate rapid equilibration, showed significant saturation hysteresis, and that very large P c (~1 MPa) are required to drain the shales. These results quantify the severity of the water blocking problem, and suggest that gas production from unconventional reservoirs is largely associated with stimulated regions that have had little or no exposure to injected water. Finally, gravity drainage of water from fractures residing above horizontal wells reconciles gas production in the presence of largely unrecovered injected water, and is discussed in the broader context of unsaturated flow in fractures.

58 GEOSCIENCES↗