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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗
DOE OSTI2015

Relative permeability of hydrate-bearing sediments from percolation theory and critical path analysis: theoretical and experimental results

Relative permeabilities to water and gas are important parameters for accurate modeling of the formation of methane hydrate deposits and production of methane from hydrate reservoirs. Experimental measurements of gas and water permeability in the presence of hydrate are difficult to obtain. The few datasets that do exist suggest that relative permeability obeys a power law relationship with water or gas saturation with exponents ranging from around 2 to greater than 10. Critical path analysis and percolation theory provide a framework for interpreting the saturation-dependence of relative permeability based on percolation thresholds and the breadth of pore size distributions, which may be determined easily from 3-D images or gas adsorption-desorption hysteresis. We show that the exponent of the permeability-saturation relationship for relative permeability to water is related to the breadth of the pore size distribution, with broader pore size distributions corresponding to larger exponents. Relative permeability to water in well-sorted sediments with narrow pore size distributions, such as Berea sandstone or Toyoura sand, follows percolation scaling with an exponent of 2. On the other hand, pore-size distributions determined from argon adsorption measurements we performed on clays from the Nankai Trough suggest that relative permeability to water in fine-grained intervals may be characterized by exponents as large as 10 as determined from critical path analysis. We also show that relative permeability to the gas phase follows percolation scaling with a quadratic dependence on gas saturation, but the threshold gas saturation for percolation changes with hydrate saturation, which is an important consideration in systems in which both hydrate and gas are present, such as during production from a hydrate reservoir. Our work shows how measurements of pore size distributions from 3-D imaging or gas adsorption may be used to determine relative permeabilities.

Daigle, Hugh↗
DOE OSTI2017

Frictional stability-permeability relationships for fractures in shales

There is wide concern that fluid injection in the subsurface, such as for the stimulation of shale reservoirs or for geological CO 2 sequestration (GCS), has the potential to induce seismicity that may change reservoir permeability due to fault slip. However, the impact of induced seismicity on fracture permeability evolution remains unclear due to the spectrum of modes of fault reactivation (e.g., stable versus unstable). As seismicity is controlled by the frictional response of fractures, we explore friction-stability-permeability relationships through the concurrent measurement of frictional and hydraulic properties of artificial fractures in Green River shale (GRS) and Opalinus shale (OPS). We observe that carbonate-rich GRS shows higher frictional strength but weak neutral frictional stability. The GRS fracture permeability declines during shearing while an increased sliding velocity reduces the rate of permeability decline. By comparison, the phyllosilicate-rich OPS has lower friction and strong stability while the fracture permeability is reduced due to the swelling behavior that dominates over the shearing induced permeability reduction. Furthermore, we conclude that the friction-stability-permeability relationship of a fracture is largely controlled by mineral composition and that shale mineral compositions with strong frictional stability may be particularly subject to permanent permeability reduction during fluid infiltration.

58 GEOSCIENCES↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2018

Assignment and calibration of relative permeability by hydrostratigraphic units for multiphase flow analysis, case study: CO 2 -EOR operations at the Farnsworth Unit, Texas

Included in the most critical factors for geological CO 2 storage site screening, selection, and operation is effective simulations of multiphase flow and transport. Relative permeability is probably the greatest source of potential uncertainty in multiphase flow simulation, second only to intrinsic permeability heterogeneity. The specific relative permeability relationship assigned greatly impacts forecasts of CO 2 trapping mechanisms, phase behavior, and long-term plume movement. A primary goal of this study is to evaluate the impacts and implications of different methods of assigning relative permeability relationships for CO 2 -EOR model forecasts. Most simulation studies published in the literature base selection of relative permeability functions on the geologic formation or rock type alone. In this study, we initially implemented reservoir model grids with previously-identified hydrostratigraphic units based on porosity and permeability relationship of the Morrow ‘B’ Sandstone, then assigned relative permeability functions for those hydrostratigraphic units. Specific, constrained relative permeability relationships were created and assigned to each hydrostratigraphic unit using petrophysical data and Mercury Intrusion Capillary Pressure (MICP) measurements, from core samples of each hydrostratigraphic unit. Results of forward simulations with the newly-calibrated models will be compared to those of previous models as well as to simulation results for a range of different relative permeability relationships. The study site is the Farnsworth Unit (FWU) in the northeast Texas Panhandle, an active CO 2 -EOR operation. The target formation is the Morrow ‘B’ Sandstone, a clastic formation composed of medium to course sands.

58 GEOSCIENCES↗
DOE OSTI2015

Predicting the permeability of sediments entering subduction zones

Abstract Using end‐member permeabilities defined by a worldwide compilation of sediment permeabilities at convergent margins, we compare permeability predictions using a geometric mean and a two‐component effective medium theory (EMT). Our implementation of EMT includes a threshold fraction of the high‐permeability component that determines whether flow occurs dominantly in the high‐ or low‐permeability component. We find that this threshold fraction in most cases is equal to the silt + sand‐sized fraction of the sediment. This suggests that sediments undergoing primary consolidation tend to exhibit flow equally distributed between the high‐ and low‐permeability components. We show that the EMT method predicts permeability better than the weighted geometric mean of the end‐member values for clay fractions <0.6. This work provides insight into the microstructural controls on permeability in subducting sediments and valuable guidance for locations which lack site‐specific permeability results but have available grain‐size information.

Daigle, Hugh↗
DOE OSTI2019

Calibration of NMR porosity to estimate permeability in carbonate reservoirs

Considering the performance of carbon dioxide (CO 2 ) stored in geologic reservoirs requires some knowledge of the subsurface heterogeneity, where the most reliable data is extracted from wellbore measurements. This benefit is especially true for carbonate reservoirs were reactions between supercritical CO 2 , the reservoir brine, and carbonate minerals create new storage capacity and enhanced permeability. Techniques like NMR well logging hold promise for estimating subsurface permeability, because the total porosity and pore size can be calculated from response of 1 H NMR signal. Resulting depth profiles of porosity and permeability from downhole logs are useful input for reservoir simulations and can be used to design injection protocols and estimate storage capacity. We conducted a detailed characterization study to quantify the NMR response to matrix pore space, the connectivity to larger vugs and fractures, and the resulting permeability of core samples from distinct carbonate formations. The cores used in study span 5 orders in permeability, 4 orders in mean T 2 , and 2%–42% in porosity. The goal of the study was to use independent measures of pore volumes, surface relaxivity, fluid-accessible surface areas, and permeability to develop carbonate permeability models. It was not possible to develop a single model that reproduced permeability for different carbonate formations, because phenomena specific to the reservoirs could not be extracted even with the state-of-the-art characterization techniques used in this study. Estimates of permeability require calibration. Best calculations for a given formation were achieved by using a simple form of the Kozeny equation (Eq. 6): κ=Α’θ 3*n , and calibrating A’ and n with laboratory measurements made on the formation cores. Empirical calibrations can then be used to map the vertical heterogeneity of the storage complex to better understand the physical movement CO 2 and brine in the subsurface and the positive feedback between mineral dissolution and the development of flow pathways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Fractured rock stress‐permeability relationships from in situ data and effects of temperature and chemical‐mechanical couplings

Abstract The purpose of this paper is to (i) review field data on stress‐induced permeability changes in fractured rock; (ii) describe estimation of fractured rock stress‐permeability relationships through model calibration against such field data; and (iii) discuss observations of temperature and chemically mediated fracture closure and its effect on fractured rock permeability. The field data that are reviewed include in situ block experiments, excavation‐induced changes in permeability around tunnels, borehole injection experiments, depth (and stress) dependent permeability, and permeability changes associated with a large‐scale rock‐mass heating experiment. Data show how the stress‐permeability relationship of fractured rock very much depends on local in situ conditions, such as fracture shear offset and fracture infilling by mineral precipitation. Field and laboratory experiments involving temperature have shown significant temperature‐driven fracture closure even under constant stress. Such temperature‐driven fracture closure has been described as thermal overclosure and relates to better fitting of opposing fracture surfaces at high temperatures, or is attributed to chemically mediated fracture closure related to pressure solution (and compaction) of stressed fracture surface asperities. Back‐calculated stress‐permeability relationships from field data may implicitly account for such effects, but the relative contribution of purely thermal‐mechanical and chemically mediated changes is difficult to isolate. Therefore, it is concluded that further laboratory and in situ experiments are needed to increase the knowledge of the true mechanisms behind thermally driven fracture closure, and to further assess the importance of chemical‐mechanical coupling for the long‐term evolution of fractured rock permeability.

58 GEOSCIENCES↗
DOE OSTI2020

Zero-Valent Iron Permeable Reactive Barrier to Remediate Volatile Organic Compounds in Groundwater - 20209

The US DOE-SRS, the US EPA, and the South Carolina Department of Health and Environmental Control determined it was appropriate to perform a non-time critical removal action at the P-Area Groundwater Operable Unit at the SRS to reduce the mass and downgradient transport of trichloroethylene in the P-Area groundwater plume. Contaminated groundwater discharges to a nearby stream, Steel Creek, within the SRS boundaries, resulting in trichloroethylene concentrations above the maximum contaminant level. Impact to surface water is limited in areal extent and supported by recently collected characterization data. The P-Area Groundwater Operable Unit encompasses the groundwater beneath an industrial area within SRS, P Area, where the P-Reactor once operated. The boundaries of the P-Area Groundwater Operable Unit extend northwest to Steel Creek, northeast toward PAR Pond, and southeast to Meyers Branch. Groundwater in the Upper Three Runs Aquifer of the P-Area Groundwater Operable Unit has been impacted by reactor and facility operations between 1954 and 1991, including tritium and volatile organic compounds. The P-Area surface units contributing to groundwater contamination were remediated as part of the P-Area Operable Unit in 2011. The P-Reactor closure is one of the first of its kind in the DOE Complex and is one of only a few full-sized production reactors in the US to undergo completion of final closure activities. The nature and extent of groundwater contamination was determined using a variety of investigative approaches such as groundwater monitoring wells, direct-push technology, and surface water samples. Groundwater contamination associated with trichloroethylene is primarily exhibited in a narrow plume that extends from the source area at P-Reactor and west to Steel Creek. Maximum contaminant level exceedances in groundwater occur over an area of ∼6.9 hectares for trichloroethylene with concentrations as high as 7.7 milligrams per liter. To the west of the P-Area facility area, the trichloroethylene groundwater plume is controlled by a buried geologic feature, assumed to be an old stream bed, that further narrows the groundwater plume in what has been designated as the 'neck area.' This narrowing of the groundwater plume provides an ideal location for a treatment barrier. The non-time critical removal action alternative chosen is to install a zero-valent iron permeable reactive barrier within the neck area of the trichloroethylene groundwater plume, perpendicular to groundwater flow direction. This technology will provide a treatment barrier that will reduce trichloroethylene groundwater concentrations by 90% and has an anticipated useful life of at least 25 years. A pre-design investigation was performed in the neck area to confirm site lithology, hydrogeology, geochemistry, and extent of trichloroethylene contamination prior to a final design. A treatability study, conducted as part of the pre-design investigation, indicated that the subsurface and groundwater in the PArea Groundwater Operable Unit is compatible with the zero-valent iron and will not lead to excessive buildup from mineralization/precipitation or biofouling. Probabilistic modeling was conducted using field and laboratory data to determine the expected performance of the zero-valent iron permeable reactive barrier. The model simulations indicated that a 3.81-centimeter thick barrier would provide greater than 90% reduction of trichloroethylene groundwater concentration. The final design of the zero-valent iron permeable reactive barrier is a barrier that will extend 80.5 linear meters in a 'zigzag' orientation to best transect the trichloroethylene plume and account for varying groundwater flow. The barrier will be installed from 13.7 meters below ground surface to 41.1 meters below ground surface for 65.8 linear meters and from 13.7 meters below ground surface to 36.6 meters below ground surface for 14.6 linear meters, the base of which is 'keyed' into a low permeability zone. The barrier is designed to a thickness of 10.2 centimeters, which was determined to reduce the trichloroethylene groundwater concentrations by greater than 90% with a safety factor of 2.67. A total of approximately 689 metric tons of zero-valent iron will be injected through 22 injection wells spaced 3.66 meters apart, using guar to suspend the zero-valent iron. Zero-valent iron permeable reactive barrier construction will be monitored through 23 installed resistivity receivers offset 7.32 meters from the zerovalent iron permeable reactive barrier. The zero-valent iron will be energized with a low-voltage 100 Hertz signal during injection and will be monitored using the resistivity receivers to ensure complete coalescence of the zero-valent iron permeable reactive barrier. The zero-valent iron was sized to have a hydraulic conductivity greater than the natural subsurface, thus promoting groundwater flow through the barrier. As contaminated groundwater contacts the zero-valent iron, volatile organic compounds, including trichloroethylene, are immediately degraded to harmless compounds such as ethylene. The performance of the zero-valent iron permeable reactive barrier will be monitored using three upgradient monitoring well clusters, six downgradient monitoring well clusters, and four in-wall monitoring wells. The in-wall monitoring wells will indicate immediate reduction of trichloroethylene mass in the groundwater and allow analyses of the zero-valent iron permeable reactive barrier health. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Impact of Three-Phase Relative Permeability and Hysteresis Models on Forecasts of Storage Associated With CO 2 -EOR

Geological CO 2 sequestration in conjunction with enhanced oil recovery (CO 2 -EOR) includes complex multiphase flow processes compared to CO 2 storage in deep saline aquifers. Two of the most critical factors affecting multiphase flow in CO 2 -EOR are three-phase relative permeability and associated hysteresis, both of which are difficult to measure and are usually represented by numerical interpolation models. The purpose of this research is to improve understanding of (1) the relative impacts of different three-phase relative permeability models and hysteresis models on CO 2 trapping mechanisms, and (2) uncertainty associated with these two factors. Four different three-phase relative permeability models and three hysteresis models were applied to simulations of an active CO 2 -EOR site, the SACROC unit located in western Texas. To eliminate potential bias of deterministic parameters, we utilized a sequential Gaussian simulation technique to generate 50 realizations to describe heterogeneity of porosity and permeability, based on data obtained from well logs and seismic survey. Simulation results of forecasted CO 2 storage suggested that (1) the choice of three-phase relative permeability model and hysteresis model led to noticeable impacts on forecasted CO 2 sequestration capacity; (2) impacts of three-phase relative permeability models and hysteresis models on CO 2 trapping are small during the CO 2 -EOR injection period, and increase during the post-EOR CO 2 injection period; (3) the specific choice of hysteresis model is more important relative to the choice of three-phase relative permeability model; and (4) using the recommended three-phase WAG (Water-Alternating-Gas) hysteresis model may increase the impact of three-phase relative permeability models and uncertainty due to heterogeneity.

42 ENGINEERING↗
DOE OSTI2022

Wellbore cement fracture permeability as a function of confining stress and pore pressure

Cemented annulus fractures are a major leakage path in a wellbore system, and their permeability plays an important role in the behavior of fluid flow through a leaky wellbore. The permeability of these fractures is affected by changing conditions including the external stresses acting on the fracture and the fluid pressure within the fracture. Laboratory gas flow experiments were conducted in a triaxial cell to evaluate the permeability of a wellbore cement fracture under a wide range of confining stress and pore pressure conditions. For the first time, an effective stress law that considers the simultaneous effect of confining stress and pore pressure was defined for the wellbore cement fracture permeability. Here the results showed that the effective stress coefficient (λ) for permeability increased linearly with the Terzaghi effective stress ( -p) with an average of λ = 1 in the range of applied pressures. The relationship between the effective stress and fracture permeability was examined using two physical-based models widely used for rock fractures. The results from the experimental work were incorporated into numerical simulations to estimate the impact of effective stress on the interpreted hydraulic aperture and leakage behavior through a fractured annular cement. Accounting for effective stress-dependent permeability through the wellbore length significantly increased the leakage rate at the wellhead compared with the assumption of a constant cemented annulus permeability.

58 GEOSCIENCES↗
DOE OSTI2015

Permeability and porosity of hydrate-bearing sediments in the northern Gulf of Mexico

Hydrate-bearing sands are being actively explored because they contain the highest concentrations of hydrate and are the most economically recoverable hydrate resource. However, relatively little is known about the mechanisms or timescales of hydrate formation, which are related to methane supply, fluid flux, and host sediment properties such as permeability. We used logging-while-drilling data from locations in the northern Gulf of Mexico to develop an effective medium theory-based model for predicting permeability based on clay-sized sediment fraction. The model considers permeability varying between sand and clay endpoint permeabilities that are defined from laboratory data. We verified the model using permeability measurements on core samples from three boreholes, and then used the model to predict permeability in two wells drilled in Walker Ridge Block 313 during the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II expedition in 2009. We found that the cleanest sands (clay-sized fraction <0.05) had intrinsic (hydrate-free) permeability contrasts of 5-6 orders of magnitude with the surrounding clays, which is sufficient to provide focused hydrate formation due to advection of methane from a deep source or diffusion of microbial methane from nearby clay layers. In sands where the clay-sized fraction exceeds 0.05, the permeability reduces significantly and focused flow is less pronounced. In these cases, diffusion of dissolved microbial methane is most likely the preferred mode of methane supply for hydrate formation. In conclusion, our results provide important constraints on methane supply mechanisms in the Walker Ridge area and have global implications for evaluating rates of methane migration and hydrate formation in hydrate-bearing sands.

58 GEOSCIENCES↗
DOE OSTI2026

Consolidation and Permeability of the B1 and D1 Gas Hydrate Bearing Sands and Associated Seal Sediments of the Extended-Duration Gas Production Test Site on the Alaska North Slope

Gas hydrate, a solid combination of gas (mostly methane in nature) and water molecules stable at low temperatures and elevated pressures, occurs naturally in marine and permafrost-associated environments. Gas hydrate reservoirs, such as those in the Alaska North Slope, have been considered potential energy resources for gas production. To understand the petrophysical and geo-mechanical characteristics of the reservoir, core samples retrieved from the site of the JOGMEC-DOE-USGS collaborative gas hydrate R&D project have been analyzed in the laboratory for their hydraulic and mechanical properties. This paper focuses on both seal and reservoir samples associated with the B1 and D1 sands, which are evaluated for index properties (including porosity, grain size distribution, liquid and plastic limits, specific surface area, and specific gravity), consolidation, permeability, and water retention. Furthermore, the reservoir core samples were tested with pore-filling, laboratory-grown tetrahydrofuran hydrate, in order to assess reservoir behavior during gas production from hydrates. Under simulated in situ stress conditions, the seal and hydrate-free reservoir cores had a permeability anisotropy ratio of k h /k v = 3.0−5.0, and k h /k v = 2.4−3.0 for the reservoir tetrahydrofuran hydrate-bearing cores. The data suggest that depressurizing the reservoir to induce hydrate dissociation alters the reservoir effective permeability in three ways: permeabilities decrease due to porosity lost (e.g., the initial reservoir thickness can decrease by up to 5% upon 7 MPa depressurization), permeability increases due to the loss of solid hydrate in the pore space, and permeability anisotropy k h /k v decreases in response to the evolving pore-space geometry. We show that given the simulated in situ gas hydrate saturations (i.e., S h = 32% in core 7P-2E and S h = 21% in core 20P-4), gas production from the dissociation of tetrahydrofuran hydrate in the two tested cores results in a net increase in effective permeability and a decrease in k h /k v . This study highlights the importance of investigating seal and reservoir sediments and the impacts of depressurization on the porosity and permeability responses during production.

Geological materials↗
DOE OSTI2024

Atomic view of photosynthetic metabolite permeability pathways and confinement in synthetic carboxysome shells

Carboxysomes are protein microcompartments found in cyanobacteria, whose shell encapsulates rubisco at the heart of carbon fixation in the Calvin cycle. Carboxysomes are thought to locally concentrate CO 2 in the shell interior to improve rubisco efficiency through selective metabolite permeability, creating a concentrated catalytic center. However, permeability coefficients have not previously been determined for these gases, or for Calvin-cycle intermediates such as bicarbonate (HCO$^–_3$), 3-phosphoglycerate, or ribulose-1,5-bisphosphate. Starting from a high-resolution cryogenic electron microscopy structure of a synthetic β-carboxysome shell, we perform unbiased all-atom molecular dynamics to track metabolite permeability across the shell. The synthetic carboxysome shell structure, lacking the bacterial microcompartment trimer proteins and encapsulation peptides, is found to have similar permeability coefficients for multiple metabolites, and is not selectively permeable to HCO$^–_3$ relative to CO 2 . To resolve how these comparable permeabilities can be reconciled with the clear role of the carboxysome in the CO 2 -concentrating mechanism in cyanobacteria, complementary atomic-resolution Brownian Dynamics simulations estimate the mean first passage time for CO 2 assimilation in a crowded model carboxysome. Despite a relatively high CO 2 permeability of approximately 10 -2 cm/s across the carboxysome shell, the shell proteins reflect enough CO 2 back toward rubisco that 2,650 CO 2 molecules can be fixed by rubisco for every 1 CO 2 molecule that escapes under typical conditions. The permeabilities determined from all-atom molecular simulation are key inputs into flux modeling, and the insight gained into carbon fixation can facilitate the engineering of carboxysomes and other bacterial microcompartments for multiple applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Permeability of membranes to amino acids and modified amino acids: mechanisms involved in translocation

The amino acid permeability of membranes is of interest because they are one of the key solutes involved in cell function. Membrane permeability coefficients (P) for amino acid classes, including neutral, polar, hydrophobic, and charged species, have been measured and compared using a variety of techniques. Decreasing lipid chain length increased permeability slightly (5-fold), while variations in pH had only minor effects on the permeability coefficients of the amino acids tested in liposomes. Increasing the membrane surface charge increased the permeability of amino acids of the opposite charge, while increasing the cholesterol content decreased membrane permeability. The permeability coefficients for most amino acids tested were surprisingly similar to those previously measured for monovalent cations such as sodium and potassium (approximately 10(-12)-10(-13) cm s-1). This observation suggests that the permeation rates for the neutral, polar and charged amino acids are controlled by bilayer fluctuations and transient defects, rather than partition coefficients and Born energy barriers. Hydrophobic amino acids were 10(2) more permeable than the hydrophilic forms, reflecting their increased partition coefficient values. External pH had dramatic effects on the permeation rates for the modified amino acid lysine methyl ester in response to transmembrane pH gradients. It was established that lysine methyl ester and other modified short peptides permeate rapidly (P = 10(-2) cm s-1) as neutral (deprotonated) molecules. It was also shown that charge distributions dramatically alter permeation rates for modified di-peptides. These results may relate to the movement of peptides through membranes during protein translocation and to the origin of cellular membrane transport on the early Earth.

Review↗
DOE OSTI2018

The influence of Preslip Sealing on the Permeability Evolution of Fractures and Faults

The evolution of permeability on fractures and faults during the full earthquake cycle is shown to be sensitive to sealing during the repose phase. We explore the combined effect of static loading followed by fracture reactivation on permeability evolution via slide-hold-slide experiments. During the hold periods, permeability exhibits a slow but continuous reduction. The permeability decay is consistent with power law compaction of the aperture coupled with cubic law flow. With increasing hold periods, permeability evolves following reactivation from net reduction to net increase with the magnitude of the permeability change dependent on the hold period. This implies that the tight interlocking of asperities during interseismic repose primes the fault for permeability enhancement following reactivation. The inferred mechanism is via shear dilation with the probable involvement of unclogging. This result identifies that preslip sealing during repose is an essential component in the cyclic permeability evolution throughout the seismic cycle.

58 GEOSCIENCES↗