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NASA NTRS1998

Issues in the reconstruction of environmental doses on the basis of thermoluminescence measurements in the Techa riverside

The potential of thermoluminescence measurements of bricks from the contaminated area of the Techa river valley, Southern Urals, Russia, for reconstructing external exposures of affected population groups has been studied. Thermoluminescence dating of background samples was used to evaluate the age of old buildings available on the river banks. The anthropogenic gamma dose accrued in exposed samples is determined by subtracting the natural radiation background dose for the corresponding age from the accumulated dose measured by thermoluminescence. For a site in the upper Techa river region, where the levels of external exposures were extremely high, the depth-dose distribution in bricks and the dependence of accidental dose on the height of the sampling position were determined. For the same site, Monte Carlo simulations of radiation transport were performed for different source configurations corresponding to the situation before and after the construction of a reservoir on the river and evacuation of the population in 1956. A comparison of the results provides an understanding of the features of the measured depth-dose distributions and height dependencies in terms of the source configurations and shows that bricks from the higher sampling positions are likely to have accrued a larger fraction of anthropogenic dose from the time before the construction of the reservoir. The applicability of the thermoluminescent dosimetry method to environmental dose reconstruction in the middle Techa region, where the external exposure was relatively low, was also investigated.

NASA Discipline Radiation Health↗

DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗

NASA NTRSDate not supplied

Flight Test Exploration of Integrated Wildfire Response Operations with Crewed and Uncrewed Air Assets

Since 2016, Japan Aerospace Exploration Agency (JAXA) and NASA have partnered to investigate the safe and efficient integration of uncrewed aircraft systems (UAS) in disaster relief operations. Current disaster response operations would benefit from the use of UAS but are limited by the need for safe airspace integration technologies and enabling regulations. The work presented in this paper focuses on the safe integration of UAS and helicopters in active wildfire disaster response operations. The authors propose the use of mission planning and traffic management technologies, including onboard pilot situation awareness support technologies, to coordinate airspace operations between crewed and uncrewed aircraft (e.g., UAS). A representative wildfire scenario including a helicopter and UAS was developed. The helicopter mission included a flight path that mimicked the siphoning of water from a natural water reservoir (lake) and a subsequent water drop over vegetation along the progressing edge of a fire perimeter. Simulated UAS were assumed to conduct aerial ignition flights in the vicinity as part of a controlled burn of the vegetation ahead of the fire front to create a natural barrier between the fire and a nearby populated area. A situation awareness tool was developed and used to support the helicopter pilots’ task to stay clear of the volume of airspace where the UAS was conducting its operation while efficiently conducting the fire suppression mission. A flight test using JAXA’s experimental helicopter was conducted in August 2022 to validate the developed technology. The advantages of the developed technology were demonstrated, and pilot feedback confirmed that real-time awareness of the UAS operation airspace helped the flight planning and execution of a safe mission that minimized the flight time loss due to the potential interference with the UAS. Post-flight interviews with the pilots indicated no sufficient increase in the pilots’ workload and provided ideas for future technology development. Future research will provide recommendations to standards development organizations to support considerations for crewed-uncrewed interactions in disaster-response operations.

disaster response↗

DOE OSTI2019

A visco-plastic framework for interface processes in sedimentary reservoir rocks at HPHT conditions

As energy operations face the challenge of reservoirs at ever-increasing depths, modelling the response of reservoir rocks at high pressure and high temperature (HPHT) conditions is a crucial step for successfully unlocking new resources. At these conditions, rocks can experience thermal and pressure sensitivity, as well as admit internal phase changes at the pore-solid interfaces that determine their macroscopic response to external loading. It is therefore expected that constitutive laws of Geomechanics should be enriched to accommodate such effects. In this work, a multi-physics constitutive theory for sedimentary rocks is proposed within the general framework of viscoplasticity. The viscosity of the material is assumed to be a function of the temperature, pore-pressure and energy required to alter the inter-granular interfaces. This energy is expressed through the chemical potentials of the phases involved during processes like chemical dissolution/precipitation of the interfaces or mechanical debonding. The resulting flow law and corresponding stress equilibrium are coupled to the energy and mass conservation laws, constituting a closed system of equations, which is solved using the Finite Element simulator REDBACK. A series of numerical tests for performance and calibration is then successfully performed against different types of reservoir rocks, confining pressures and temperatures (from room temperature to over 800K). Here, we show that the mechanical response of sedimentary porous rocks at strains usually achieved in laboratory testing can be explained by accounting for the interface processes taking place at the cementitious material bonding the grains, a process that can also determine the brittle-to-ductile transition of sedimentary rocks.

58 GEOSCIENCES↗

DOE OSTI2020

Coupled Thermo–Hydro–Mechanical Modeling of Hydro-Shearing Stimulation in an Enhanced Geothermal System in the Raft River Geothermal Field, USA

The Raft River Enhanced Geothermal System (EGS) demonstration project aims to improve the geothermal production by enhancing the reservoir permeability via shear stimulation in well RRG-9. In this paper, we performed a series of 3D thermo– hydro–mechanical (THM) simulations to investigate the influence of hydraulic and thermal effects on the development of this EGS. The model includes synthetic fracture populations based on borehole televiewer images and in-situ stress measurements from well RRG-9. Fracture permeability evolution is determined using an empirical permeability law developed from laboratory experiments. The model was calibrated by comparing the hydraulic response to field observations, including wellhead pressure, injection rate, and well injectivity. Particularly, we analyzed the enhancement of reservoir permeability and the spatial extent of the stimulation zone for the given injection schedule. Our results indicate that the permeability enhancement of fractured geothermal reservoir is caused by the combined effects of injection-induced cooling and fluid pressure increase. The decrease of temperature plays a dominant role in reactivation of natural fractures under the hydro-shearing mechanism, while the higher injection pressure promotes shear failure and enlarges the stimulation zone. Finally, for the specified extensional stress state, the model favors greatest permeability enhancement along the maximum horizontal principal stress, a moderate vertical enhancement, and a smaller gain along the minimum horizontal principal stress.

58 GEOSCIENCES↗

NASA NTRS2024

ResORR: A Globally Scalable and Satellite Data-Driven Algorithm for River Flow Regulation Due to Reservoir Operations

We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. On average, ResORR improved regulated river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely software algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

River Regulation↗

DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗

DOE OSTI2020

toughio v1.2.2

TOUGH is a widely used general purpose numerical simulator designed for fluid and heat flows of multiphase, multicomponent fluid mixtures in porous and fractured media, which has been applied to many real-world problems such as underground geological storage, geothermal reservoir engineering or nuclear waste disposal, to name a few. `toughio` is a lightweight, object-oriented and vectorized Python library that aims to provide user-friendly routines to facilitate pre- and post-processing of a TOUGH simulation. Although its primary target is the latest version TOUGH3, `toughio` can also be used to read/write TOUGH2 input/output files. Currently, to the best of our knowledge, only PyTOUGH offers an exhaustive number of features to carry out a complete TOUGH simulation using a scripting language. `toughio` and PyTOUGH share the same objectives, yet with different approaches and philosophies. On the one hand, a PyTOUGH mesh is represented as a MULGRAPH geometry where elements can be unstructured horizontally but only layered vertically. It also checks that the model parameters are valid to ensure that a TOUGH simulation runs smoothly. On the other hand, although it provides basic meshing features, `toughio` mostly relies on common third-party softwares (e.g. Abaqus, FLAC3D, Gmsh, LaGriT) to generate the mesh by importing and converting it to a TOUGH mesh, which also conveniently facilitates the coupling of TOUGH with any other simulator that also supports the same mesh formats. Besides, `toughio` is designed to be just a TOUGH input and output file reader/writer. It assumes that the user knows what he is doing and does not verify the validity of the model parameters (`toughio` does verify the functional arguments).

Luu, Keurfon↗

DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗

DOE OSTI2023

FY22 Laboratory Directed Research and Development Annual Report

The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites; 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in actives impacting national security; 3) Lead ST&E as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits; 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications; 5) Build a workforce for the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

FY24 Laboratory Directed Research and Development Annual Report

The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in activities impacting national security 3) Lead Science, Technology & Engineering as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications 5) Build a workforce for the future

Clark, Sue [Savannah River National Laboratory (SR↗

NASA NTRS1997

Integration of CELSS Simulation with Long-Term Crop Scheduling

This project presents guidelines for the integration of the Intelligent Crop Scheduler (ICS) developed by Leon (1995) with a version of the Biological Life Support System (BLSS) simulation based on the work by Volk and Rummel (1987). These guidelines will also aid in defining the appropriate model detail of simulation-based schedulers. ICS determines what, when and how much to plant of different crops. These decisions are made such that the critical reservoir levels are maintained close to their nominal or desired settings during the duration of the mission. Initial feasibility of the approach has been demonstrated using simplified implementations of the scheduling approach and CELSS 'world' model developed by the investigator at JSC in the Summer of 1995. The BLSS model incorporates more detail. The increased fidelity is in terms of added mass-regeneration formalism (biochemical stoichiometry), more plants, and more accurate modeling of the mechanical system. This report describes the main features of the Crop scheduler and CELSS simulator, discusses integration issues, and provides with detailed guidelines for the integration of these two applications.

Leon, V. Jorge↗

NASA NTRS1989

Computation of rarefied hypersonic flows

Numerical techniques for the simulation of hypersonic flows of rarefied gases are examined in an analytical review. The direct-simulation Monte Carlo (DSMC) method developed to interpret measurement data obtained by the Space Shuttle in the SUMS project is described; the fundamental limitations of the DMSC approach are discussed; and modifications to improve the physical plausibility of DSMC predictions are proposed. Particular attention is given to the use of a double-peaked molecular distribution function for the internal flow in the SUMS probe, a downstream vacuum-reservoir condition as a simplifying assumption, and an explicit forward-time centered-space differencing scheme for the discretization of the SUMS problem. Typical simulation results are presented in extensive graphs and briefly characterized.

Cheng, Sin-I↗

DOE OSTI2024

Managing Subsurface Pressure Buildup and Interference in Commercial-Scale CO 2 Storage Project with Proximal Injection Wells

Large-scale decarbonization using carbon capture and storage (CCS) is likely to involve many commercial-scale CO 2 storage projects located in close proximity to each other. This close proximity raises concerns over pressure interference among the storage projects. Pressure interference between injection and storage efforts can reduce the practicable CO 2 storage resource and force wells to inject CO 2 at a lower rate to avoid the fracture pressure thresholds per United States Environmental Protection Agency (EPA) Class VI well regulations to preserve injection and confining zone integrity and potentially mitigate against inducing seismic activity. These analyses employ numerical full-physics reservoir modeling to evaluate how pressure buildup fronts and CO 2 plumes evolve under commercial-scale injection volumes of CO 2 in which multiple storage sites located in close proximity occur in tandem. The simulation models mimic injection at pseudo basin-scale and assume homogeneous saline formation(s) as storage targets with a pair of upper and lower homogeneous seal layer/s. These analyses specifically investigate the efficacy of two basin-wide reservoir pressure management strategies in addressing the technical challenges associated with pressure buildup and CO 2 plume commingling. The strategies explored include: 1) enlarging the area of injection well spacing (WS) and 2) storing CO 2 in a stacked sequence (SSS) of saline formations compared to a single formation. The storage and confining zones properties assumed were held common across the scenarios, unless specified otherwise. Analyses results show that after injecting 4 million tons per year for 30 years using 4 separate wells (each injecting 1 million metric tons per year), the radius of CO 2 plume extends to a mere 3 km or less from injection wells. Meanwhile, the radius of pressure buildup ranges on the order of tens to a few hundreds of kilometers, depending on the magnitude of pressure buildup threshold that one would use to define the front. CO 2 plume commingling from different injection wells appears to occur 50 years post-injection, especially under scenarios with narrowly spaced (i.e., < 5 km apart) injection well locations. Findings from sensitivity cases on the well spacing suggest that storage formations modeled would require different well spacing to avoid fracture pressure thresholds. For instance, modeled storage formations with high fracture gradients (i.e., 0.8 psi/ft) would need less than 5–km well spacing, whereas those with lower fracture gradients (i.e., 0.7 psi/ft) would need approximately 20–km well spacing, based on assumed modeling parameters. Under stacked injection, the pressure challenges (described above) still exist but are more alleviated due to distributing the same injection volume across more available reservoir volume. These analyses demonstrate that stacked-sequence storage can effectively address the challenges, while still providing the same target CO 2 storage volumes and allowing a large number of storage projects to be deployed in the same basin by better utilizing the available storage resource across different reservoir depths. Among cases modeled, the resulting pressure buildup front is most suppressed when each storage project distributes injection volumes over several wells, each of which injects a portion of the total CO 2 across the stacked sequence. This strategy results in the smallest CO 2 aerial footprint amongst scenarios evaluated but also shows the largest reduction in the pressure buildup at the top of perforation at the injection wells (upwards of approximately 42 percent compared to the commercial-scale single-formation storage), the result of which is crucial to maintain caprock integrity. The findings presented by this research draw attention to the importance of greater coordination among storage operators and regulatory stakeholders to foster the upscaling and deployment of CCS. These analyses provide insights into required decision-making when considering multi-project deployment in a shared basin. Because these analyses evaluate a very specific geologic situation, they bear further investigations across other geologic situations.

42 ENGINEERING↗

DOE OSTI2021

Nanoscale Interfacial Smoothing and Dissolution during Unconventional Reservoir Stimulation Implications for Hydrocarbon Mobilization and Transport

Hydraulic fracturing of low-permeability rocks significantly enhances hydrocarbon production from unconventional reservoirs. However, fluid transport through low-permeability rocks, and the influence of geochemical transformations on pore networks, are poorly constrained. Mineral reactivity during interactions with injected water may alter the physical nature of the rock, which may affect hydrocarbon mobility. To assess alteration to the rock, we previously conducted a hydrothermal experiment that reacted cubed rock samples (1 cm3) with synthetic hydraulic fracturing fluid to simulate physicochemical reactivity during hydraulic fracturing. We analyzed unreacted and reacted rocks by small-angle neutron scattering and high-pressure mercury intrusion to determine how nanoscale (~1-1400 nm) pore networks of unconventional reservoir rocks are influenced by reaction with hydraulic fracturing injectates. We show that fluid-rock interactions have a two-fold influence on hydrocarbon recovery, promoting both hydrocarbon mobilization and transport. Pore-matrix interfaces smooth via removal of clay mineral surface asperities, reducing available surface area for hydrocarbon adsorption by ca. 12-75%. Additionally, hydraulic fracturing fluid-induced dissolution creates new pores with diameters ranging in size from 800-1400 nm, increasing the permeability of the rocks by a factor of 5-10. These two consequences of mineral dissolution act in concert to release hydrocarbons from the host rock and facilitate transport through the rock during unconventional reservoir production.

Herz-Thyhsen, Ryan↗

DOE OSTI2023

Impact of organic solutes on capillary phenomena in water-CO2-quartz systems

Hypothesis: The migration of supercritical CO2 (scCO2) injected into underground reservoirs as part of carbon capture and storage is influenced by organic contamination affecting mineral wettability. Molecular dynamics (MD) simulations of relevant systems that incorporate representative organic solutes allow detailed investigation of changes in fundamental interfacial and capillary properties. Experiments: We use MD simulations to explore the effects of four organic solutes (quinoline, decanoic acid, coronene, sorgoleone) on the wettability of quartz by water in the presence of scCO2. We examine the impacts of polar, alkyl, and aromatic moieties as well as fluid flow velocity at elevated temperatures and pressures. Findings: Organic molecules accumulate at the water-CO2 interface, where they distribute according to their size and functional groups. Certain organics penetrate the adsorbed water film at the quartz-CO2 interface, revealing two modes of hydrogen bonding between polar organic functional group, water, and quartz surface –OH groups. Interfacial energies and contact angles are minimally impacted by organic adsorption at the water-CO2 interface, possibly due to simultaneous CO2 desorption. Non- equilibrium MD simulations reveal that flow-induced redistribution of organic compounds modulates the radii of curvature of the advancing and receding water-CO2 interfaces. Our results indicate that organic adsorption on water surfaces is likely ubiquitous during multi-phase flow in soils and sedimen- tary rocks, with implications for the mobilization and transport of organic compounds.

36 MATERIALS SCIENCE↗

DOE OSTI2020

River Regulation Alleviates the Impacts of Climate Change on US Thermoelectricity Production

Climate change and anthropogenic activities can both influence stream temperature, which is a critical indicator of thermoelectric power generation potential. Previous studies emphasized the vulnerability of power-generation systems to climate warming (ref 1, 2). Despite the fact that two-thirds of the global rivers are regulated by dams (ref 3), the relative impacts of water management and climate change on stream temperature and thermoelectric production have not yet been evaluated. Using an integrated modeling system applied to the conterminous United States, simulations confirm the dominant role of climate change in shaping the future trends of stream temperature. However, water management, reservoir regulation in particular, can mitigate the stream temperature warming and reduce the frequency and magnitude of extreme high stream temperatures through redistribution of the seasonal streamflow. Moreover, water management can alleviate the loss of power plant usable capacity from climate change to the level of reduced warming from emission mitigation. By regulating the flow and temperature regimes, water management is a promising adaptation measure to alleviate climate change induced effects in the water-energy nexus.

Zhang, Xiao↗

DOE OSTI2022

Lost Circulation Materials in Geothermal Drilling: Thermal Degradation, Viscosity, Compression, and Flow Loop Tests

This dataset provides a set of experimental data on the performance of lost circulation materials (LCMs) used in geothermal drilling operations. It includes results from four distinct tests: thermal degradation, viscosity measurements, compression tests, and high-temperature flow loop experiments. The materials tested, such as cedar fiber, cotton seed hulls, magma fiber, microcellulose, and sawdust, were analyzed under a range of conditions to assess their effectiveness in mitigating fluid losses in geothermal wells. Data on thermal degradation explores mass loss, water loss, and gas release for each material when subjected to temperatures between 90C and 250C. Viscosity measurements capture the behavior of LCM mixtures under different temperatures, while compression tests investigate the structural integrity of both undegraded and thermally degraded materials. The high-temperature flow loop experiments simulate geothermal well conditions, analyzing how different materials and mixtures affect fluid flow and sealing performance in a fractured reservoir environment. In addition to the experimental data, the dataset includes photos documenting the materials, schematics of test setups, and several published manuscripts that provide detailed insights into the experimental methods and findings.

15 GEOTHERMAL ENERGY↗