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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Experimental, Economic, and Environmental Assessment of Feedstock Gasification for High-Octane Fuel Production

Five economically advantaged biomass feedstocks identified in the US Department of Energy's 2016 Billion Ton Study were gasified and the syngas reformed at the bench scale to study the feedstock price-performance relationship. The distribution of reformed syngas compositions, heating values, and yields were similar across the different feedstocks and blends thereof, which ranged from inexpensive residual wastes to more expensive and higher quality biomass, revealing that feedstock performance was mostly insensitive to its price. Custom blended feedstocks produced syngas with characteristics resembling linear combinations of syngas from single-component feedstocks, supporting the ability to customize and predict blended properties based on single-feedstock data. A techno-economic analysis of specific feedstock costs for producing high-octane gasoline showed that miscanthus and forest residues were the most cost-effective. A field-to-wheels life-cycle assessment of greenhouse gas emissions showed that forest residues was the most environmentally benign feedstock of those studied.

09 BIOMASS FUELS↗

DOE OSTI2020

Improved plutonium and americium photon branching ratios from microcalorimeter gamma spectroscopy

Photon branching ratios are critical input data for activities such as nuclear materials protection and accounting because they allow material compositions to be extracted from measurements of gamma-ray intensities. Uncertainties in these branching ratios are often a limiting source of uncertainty in composition determination. Here, we use high statistics, high resolution - (60–70 eV full-width-at-half-maximum at 100 keV) gamma-ray spectra acquired using microcalorimeter sensors to substantially reduce the uncertainties for 11 plutonium ( 238 Pu, 239 Pu, 241 Pu) and 241 Am branching ratios important for material control and accountability and nuclear forensics in the energy range of 125 keV to 208 keV. Lastly, we show a reduction in uncertainty of over a factor of three for one branching ratio and a factor of 2–3 for four branching ratios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Covalency of M–N Bonds in Isomorphous Lanthanide and Actinide 5-(2-Pyridyl)-1H-tetrazolate Complexes

Experimental and computational analyses of [M(pdtz) 3 (H 2 O) 3 ]·3.5H 2 O (M 3+ = Pu 3+ −Cm 3+ , La 3+ −Nd 3+ , and Sm 3+ −Ho 3+ , pdtz− = 5-(2-pyridyl)-1H-tetrazolate) were conducted to understand potential differences in bonding between lanthanide and actinide complexes with a N-donor ligand. Structural analyses show that the An−N bond distances in the Pu 3+ , Am 3+ , and Cm 3+ complexes are within error of one another. Whereas in the lanthanide series, there is a nearly linear decrease in the Ln−N bond lengths from La 3+ to Ho 3+ (excluding Pm 3+ ). The An−N bond lengths are ∼0.015 Å shorter than their similarly-sized lanthanide analogs, in agreement with computational results that suggest greater covalent character in these bonds versus those with lanthanides. QTAIM analysis indicates that the An−N orbital mixing remains essentially unchanged from Pu 3+ to Cm 3+ , consistent with the nearly identical An−N bond lengths. However, upon deconvolution of the NLMOs into orbital compositions, the metal orbital contributions to An−N bonding decreases slightly overall wherein the 6d involvement remains constant, 7s involvement slightly increases, and 5f participation decreases. The molecular orbital energy diagram indicates that energy degeneracy between the 5f metal and 2p ligand orbitals increases from Pu 3+ to Cm 3+ and counteracts the contraction of the 5f orbtials. Together with prior reports of decreasing energy degeneracy between 5f and 3p orbitals from Np 3+ to Cf 3+ , these observations provide guidance on understanding how chemical bonding evolves in the actinide series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

CasZ compositions and methods of use

Provided are compositions and methods that include one or more of: (1) a “CasZ” protein (also referred to as a CasZ polypeptide), a nucleic acid encoding the CasZ protein, and/or a modified host cell comprising the CasZ protein (and/or a nucleic acid encoding the same); (2) a CasZ guide RNA that binds to and provides sequence specificity to the CasZ protein, a nucleic acid encoding the CasZ guide RNA, and/or a modified host cell comprising the CasZ guide RNA (and/or a nucleic acid encoding the same); and (3) a CasZ transactivating noncoding RNA (trancRNA) (referred to herein as a “CasZ trancRNA”), a nucleic acid encoding the CasZ trancRNA, and/or a modified host cell comprising the CasZ trancRNA (and/or a nucleic acid encoding the same).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

CASZ compositions and methods of use

Doudna, Jennifer A.↗

DOE OSTI2020

3D-printing methacrylate/chitin nanowhiskers composites via stereolithography: Mechanical and thermal properties

We report on the mechanical and thermal properties of stereolithography (SLA) 3D-printed methacrylate (MA) resin reinforced by chitin nanowhiskers (CNWs). CNWs were synthesized by acid hydrolysis of crab shell chitin and nanocomposites with 0 wt%, 0.5 wt%, 1.0 wt%, and 1.5 wt% CNWs loading were prepared by slurry compounding method. The obtained CNWs (diameter = 23 ± 5 nm, length = 253 ± 100 nm, and aspect ratio = 11 ± 3) are well dispersed within the bulk of MA at 1.0 wt% loading, implying good interfacial adhesion between the nanofiller and the resin matrix possibly due to hydrogen bonding interactions. The addition of up to 1.0 wt% CNWs to neat MA enhances the tensile strength, strain at break, modulus, and maximum thermal degradation temperatures of the nanocomposites. Moreover, the incorporation of CNWs not only improves the mechanical and thermal properties, but also preserves the excellent resolution and accuracy of 3D-printed MA/CNWs nanocomposites.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Hydrothermal catalysis of waste greases into green gasoline, jet, and diesel biofuels in continuous flow supercritical water

Production of green gasoline, jet, and diesel biofuels from waste greases was achieved using a novel hydrothermal, continuous-flow catalytic process operating under supercritical water conditions, with recycled water the only added chemical. Thermally and chemically stable catalysts were explored to optimize yields of liquid biofuels and to minimize production of gases and acidic compounds. A 50:50 mixture of brown and yellow waste greases converted into 76.6 wt% liquid biocrude (BC); the remainder converted to water and gases. Less than 0.2% of the FS formed carbon char (mainly amongst catalyst particles). Various tubular reactors (Inconel, ® Hastelloy®, titanium, stainless) showed no interior defects, erosion, or mass loss after runs. Here, the titanium catalyst was fully recovered and regenerated back to its original potency. The BC was further refined into 28%, 48%, 20%, and 4 wt%, respectively for green gasoline, jet, diesel, and bunker. Biofuels were analyzed for compound class compositions and reaction mechanisms were proposed. The hundreds of identified fuel products (C3-C35) from processing oleic acid as a pure model compound were identified. The neat green gasoline and diesel biofuels along with a 50% green jet blend (with petroleum Jet A) were tested in appropriate spark ignition, turbine, and diesel engines at University of MN Engine Labs. Biofuels achieved 107.7%, 97.2%, and 101.3% engine power performance levels relative to petroleum fuels (91-Octane, Jet A, #2 Diesel) along with lower CO and pollutant emissions. Biofuels passed ASTM fuel specifications (D4814, D7566, D975) including mandated corrosion and low sulfur limits of all three biofuels.

09 BIOMASS FUELS↗

DOE OSTI2020

A multiscale cohesive law for carbon fiber networks

Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. In this study, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Systematic Validation of Ensemble Cloud-Process Simulations using Polarimetric Radar Observations and Simulator over the NASA Wallops Flight Facility

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long-term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the WFF site via a bilateral approach of storm-scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud-process simulations through mixed-phase, light-rain, and deep-convective precipitation cases. The framework consists of creating a domain-shifted ensemble of large-scale forcing datasets, and configuring and performing cloud-process simulations with three different bulk microphysics schemes. Validation uses NASA S-band dual-POLarimetric radar (NPOL) observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble-based validation framework demonstrates that 1) no particular forcing or microphysics scheme outperforms the rest and 2) the skill scores of coarse- and fine-resolution ensemble simulations with different domain-shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse-resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations (LESs) in support of aircraft and satellite instrument development as well as cloud-precipitation-convection parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗

DOE OSTI2022

Numerical simulations of membrane distillation systems with actively heated membranes

Membrane distillation (MD) is a thermal desalination process that is gaining attention for treating hypersaline brines. One recent approach uses composite membranes with a thermally conductive layer that delivers heat to the membrane–feed interface, where it drives evaporation. Though this increases single-pass recovery, it comes with the challenge of promoting lateral heat conduction through the thin membrane. Herein, we develop a 3D, computational fluid dynamics (CFD) model that simulates conjugate heat, mass, and momentum transport in the feed channel and composite membrane. We then use the CFD to verify a simpler numerical model that approximates the feed velocity field analytically. We validate the numerical model experimentally, and use it to investigate the impacts of the conductive layer thickness, feed channel geometry, and operating conditions on temperature and concentration polarization, permeate production, and specific energy consumption. Overall, we find that lateral heating increases permeate production at the expense of increased concentration polarization. In extreme cases, the concentration increases more than four-fold along the membrane surface. Furthermore, we show however, that the feed flow rate and conductive layer thickness can be tailored to mitigate concentration polarization, for only a small reduction in permeate production.

42 ENGINEERING↗

DOE OSTI2025

Dissolved CO 2 Modulates the Electrochemical Capacitance on Gold Electrodes

The presence of CO 2 at an electrified interface between an aqueous electrolyte and a metal electrode is the prerequisite for many electrochemical CO 2 capture technologies. To understand the behavior of dissolved CO 2 at an aqueous electrified interface, we characterized the electrochemical interface of planar gold electrodes with cyclic voltammetry, electrochemical impedance spectroscopy (EIS), electrochemical surface plasmon resonance (EC–SPR), and attenuated total reflectance surface-enhanced infrared spectroscopy (ATR–SEIRAS). Under all investigated conditions, we observed a decrease in the electrochemical capacitance upon saturation of the electrolyte with CO 2 , as compared to an electrolyte saturated with Ar. EIS and EC–SPR showed that this capacitance reduction was also potential dependent: it reached a minimum near the point of zero charge and became more significant as the applied potential moved further away from the point of zero charge. Hybrid quantum–classical simulations of the gold/aqueous electrolyte interface indicate that bicarbonate decreases the capacitance and modifies the composition of the electric double layer. In addition to the binding of bicarbonate under positive bias, we propose that molecular CO 2 can be induced by applied potential to concentrate in the diffuse layer of the electric double layer, leading to a reduction in the electrochemical capacitance under both negative and positive bias. Furthermore, this work advances the understanding of non-Faradaic effects of dissolved CO 2 at aqueous electrified interfaces of relevance for electrochemical CO 2 capture.

25 ENERGY STORAGE↗

DOE OSTI2022

Spatiotemporal Controls on the Delivery of Dissolved Organic Matter to Streams Following a Wildfire

Warmer and drier climate has contributed to increased occurrence of large, high severity wildfires in the Pacific Northwest, drawing concerns for water quality and ecosystem recovery. While nutrient fluxes generally increase post-fire, the composition of organic matter (OM) transported to streams immediately following a fire is poorly constrained, yet can play an integral role in downstream water quality and biogeochemistry. Here, we quantified the spatiotemporal patterns of dissolved OM (DOM) chemistry for five streams burned by wildfires in Oregon, USA in 2020. We sampled over a 24-hour storm event one month after the fire, revealing variable temporal behavior in DOM dynamics. DOM chemistry was directly related with burn severity spatially. Specifically, nitrogen and aromatic character of DOM increased in streams burned at greater severity. Our results suggest a spatial overprinting of DOM dynamics immediately following fire activity and highlight a key gap in our knowledge of post-fire DOM transport to streams.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The Response of Summertime Organic Aerosol Composition to Emission Controls in the Northeastern United States

The responses of secondary organic aerosols (SOA) to emission controls remain uncertain due to the limited continuous long‐term measurements. Measurements of detailed carbonaceous aerosol composition in the New York City metropolitan region during the summers of 2001, 2009, 2011, and 2018 provide a look at the changes in summertime SOA over the past two decades when significant pollution reduction and emission controls occurred in the northeastern United States. Here, we show that the averaged nighttime SOA, especially the fresh local SOA, extreme mass concentrations resulted from anthropogenic volatile organic compounds reductions, while there is a little trend for the regional aged SOA. Meanwhile, these fresh SOA extremes occurred most often under conditions of high relative humidity during nighttime. The analysis presented here provides a comprehensive evaluation of the evolution of the increasingly important organic component of the submicron ambient aerosols in this major metropolitan area.

Zhang, Jie↗

DOE OSTI2024

CdZnTeSe: Recent advances for radiation detector applications

The quest for cost-effective, high performing room temperature semiconductor detector (RTSD) materials for high energy gamma rays has been continuing for more than three decades. The requirements for RTSD materials, however, are more stringent as compared to other applications unrelated to detection of X- and gamma-rays, mainly due to the requirement of thick detectors for sufficient absorption of high-energy electromagnetic radiation and excellent charge-transport properties. The II-VI compound CdZnTe (CZT) with the composition of 10 atomic % Zn (Cd0.9Zn0.1Te) has been the material of choice over the past several years, and it has dominated the commercial market for RTSD materials and detectors. Despite its commercial success as a room temperature radiation detection material, CZT suffers from a lack of compositional homogeneity on both a micro- and macro-scale and the presence of high concentrations of sub-grain boundary (dislocation walls) networks and secondary phases (Te-rich inclusions). This chapter focuses on the presence of performance-limiting defects in CZT that hinder the yield and elevate the cost of high-quality detectors. The presence of such performance-limiting defects has restricted widespread deployment of CZT for a variety of potential applications, particularly for uses of relatively large detectors where the demands on material perfection are significantly greater. In the recent past, replacing some of the tellurium with selenium in the CZT matrix was found to be very effective in drastically reducing the Te-rich secondary phases and dislocation networks, plus

Roy, Utpal N.↗

DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗

DOE OSTI2022

The solubility of titanite in silicate melt determined from growth and dissolution experiments

The solubility of titanite (CaTiSiO 5 ) in Si-rich melts was measured experimentally through growth experiments at 800–1000 °C, 0.5–1.0 GPa, log fO 2 ~ CCO–0.8, t = 72–168 h, and H 2 O = 0 to 4 wt.%, and in dissolution experiments at 925–1300 °C, 0.8 GPa, t = 18–118 h, and H 2 O = 1–10 wt.% in a piston-cylinder apparatus. Run product glasses in growth experiments were homogeneous, and iron loss suppressed ilmenite crystallization. Saturation concentrations in dissolution experiments were estimated by fitting measured diffusion profiles. Titanite solubility increases with increasing temperature and melt composition parameter $M = {\text{ molar }}\left( {{\text{Na }} + {\text{ K }} + {\text{ 2Ca}}} \right)/\left( {{\text{Al }} \times {\text{ Si}}} \right)$ . Multiple linear regression of glass composition data from growth and dissolution experiments ( n = 29) plus 39 experiments from the LEPR database (Hirschmann et al. 2008 ) yielded the titanite solubility equation (adj. r 2 = 0.95): $\left( {TiO_{2} } \right)^{melt} \left( {wt.\% } \right) = 0.978 \times M + 0.0048 \times T\left( K \right){-}5.90$ . This model correctly predicted undersaturation in 95% of 2344 experiments from the LEPR database that did not have titanite or rutile. Application to natural rocks yields saturation temperatures that are similar to independent temperature estimates. Finally, this equation should be useful for constraining the temperatures of titanite-saturated melts, for determining whether titanite saturation in magmatic source regions is likely, and for determining when titanite can crystallize and begin to exert an influence on melt geochemistry.

58 GEOSCIENCES↗

DOE OSTI2021

Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties

Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed ‘meta-metabolome ecology.’ Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as ‘thermodynamic redundancy,’ which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable principles, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.

Danczak, Robert E.↗