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At least 217 records · Page 12

Recommended DSMC Collision Model Parameters for Planetary Entry

Hybrid simulation frameworks combining Computational Fluid Dynamics (CFD) and Direct Simulation Monte Carlo (DSMC) are frequently employed to efficiently perform high-fidelity simulations of environments consisting of both continuum and rarified flow. DSMC is a stochastic, particle-based method which solves the fundamental Boltzmann equation and is therefore necessary for high-Knudsen flow where continuum-based assumptions governing CFD break down. However, the DSMC methodology is generally computationally inefficient to model the continuum regime. In a CFD/DSMC hybrid approach, obtaining an accurate, high-fidelity solution hinges on the consistent treatment of transport properties and the thermo-chemical models employed within the two solvers. In principle, in regions where CFD and DSMC are both employed, the same gas mixture under the same conditions should have the same properties, regardless of simulation type. Observed differences should be due to non-equilibrium processes, rather than differences in physical models. The goal of this work is to provide a comprehensive DSMC transport database encompassing the vast majority of known gas species encountered during Earth or other planetary atmospheric entry. This goal is largely possible due to recently performed ab initio quantum chemistry calculations. Combined with other high-fidelity data, the planned database will consist of collision integral data for over 200 neutral and ionized species and over 20000 binary collisions. From these collision integrals, Nelder-Mead optimization is used to compute collision-specific Variable Soft Sphere (VSS) collision model parameters, fitted from 300 K to 20000 K. Initial comparisons of transport properties of relevant equilibrium gas mixtures show great agreement between CFD and DSMC-derived results. The completed database can be readily applied to model binary collisions of any gas mixture containing the included species over the specified temperature range, making it a valuable tool for future planetary probe modeling efforts. An example is shown below in Fig. 1. Equilibrium mixture transport properties for a 35-species mixture composed originally of 10% air and 90% pyrolysis species of a carbon-phenolic ablator material [4] are computed using both fitted VSS Parameters and the original CFD collision integral values. Deviations in computed properties between the two approaches are less than 5% for the entire temperature range.

M. R. Gosma↗

Geographic information system for fusion and analysis of high-resolution remote sensing and ground data

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System (GIS), integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a boreal forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case 1) calibrated DC-8 SAR (Synthetic Aperture Radar) data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case 2) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.

Freeman, Anthony↗

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

Dissemination of Global Surface Water Mapping from SAR and Optical Data to Global Stakeholders

Under NASA Solicitation A.37 “Earth Science Applications: Disaster Risk Reduction and Response,” NASA’s Disasters Program is funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity using Earth observation (EO) datasets (SAR and optical imagery) and derived flood products. A new initiative in the Disasters Program is underway to disseminate the flood products from different sensors to global stakeholders via the Pacific Disaster Center’s DisasterAWARE®, the NASA Disasters Mapping Portal and potentially other mechanisms. This initiative will also generate an integrated flood product(s) using an ensemble approach that will combine outputs from hydrologic models and EO data for different stakeholders globally for decision-making and response efforts.

remote sensing↗

Search for new physics in the final state with a single photon and large missing transverse momentum in proton-proton collisions at √𝑠 =13 TeV

A search for new physics in events featuring a single photon and missing transverse momentum is presented, using proton-proton √𝑠 =13 TeV collision data corresponding to an integrated luminosity of 101 fb −1 collected by the CMS experiment at the CERN LHC between 2017 and 2018. This analysis, combined with a previous study of 36 fb −1 of 2016 data (totaling 137 fb −1 ), reveals no significant deviations from standard model expectations. The results are then used to establish 95% confidence level limits on parameters in theoretical models involving dark matter and large extra dimensions. Compared to the 2016-only analysis, this search achieves up to a 14% improvement in exclusion reach for mediator masses in simplified dark matter models, along with 11% and 10% enhancements in the limits on the effective field theory suppression scale and the fundamental Planck scale, respectively. These results are the most stringent constraints on these parameters to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

2023 Real-time Optimization Workflow Status Update

Nuclear integrated energy systems are composed of a diverse set of energy generation sources and exist in dynamic and competitive electricity markets. With the inclusion of thermal energy storage, nuclear power systems can store heat for future use through various processes, such as water desalination or hydrogen generation. This heat storage can be managed in such a way as to economically optimize its usage. By combining real-time price data from the day-ahead and real-time markets with predictive and intelligent models, the charging and discharging of the thermal energy storage may be determined and optimized. This research details an approach through models and systems for the real-time optimization (RTO) of capacity allocation. Virtual models of the energy system and its physics phenomenon and component interactions provide intelligence to verification and prediction of operations. An optimization framework can use data generated from both a set of physical assets as well as the virtual models to predict future performance and create optimization and control workflows. Data warehouse technologies can be used to combine data across models, optimization workflows, market price data, and sensor data to intuitively store various types of data and provide integrations to physical control systems as well as user visualizations. Put together, these components can create a system for the RTO of nuclear integrated energy systems. Various virtual models and bench-scale physical demonstrations have been successfully performed and verified using this system. Larger scale testbeds that include thermal energy storage systems have been identified as future opportunities. A gap analysis that details the steps necessary to reach a physical demonstration at this scale is provided, along with conclusions on the current effort and future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Multiscale Electricity Modeling for Evaluating Carbon Capture and Sequestration Technologies (MEME-CCS)

This effort employs and adapts an existing, rigorous multiscale electricity modeling platform at the National Renewable Energy Laboratory (NREL) to evaluate carbon capture and sequestration (CCS) and negative emissions technologies (NET) from the ARPA-E FLECCS program. NREL's modeling platform includes the Regional Energy Deployment System (ReEDS) electric sector capacity expansion model, which projects future electricity generation mixes at sub-state-level resolution that are downscaled to the unit-level to enable hourly, zonal or nodal electricity production cost modeling in the PLEXOS model. The resulting hourly price data from PLEXOS is provided to technology developers under the ARPA-E FLECCS program to enable technology-specific economic analysis. The ReEDS-PLEXOS modeling suite is well-established for examining electric sector futures with high renewable energy penetrations, energy storage, electrification, and distributed generation. The key advancement proposed herein utilizes collaboration with CCS experts at the University of Wyoming and the FLECCS teams to create innovative methods for representing CCS and NET in the ReEDS and PLEXOS models. Expanded technology options, new operational parameterizations, and detailed data defining CO2 capture, transportation, and storage systems are being integrated into these models to allow an unprecedented combination of scope and resolution for exploring the future of CCS and NET. The resulting capabilities will take advantage of high-performance computing resources to permit wide-ranging scenario analysis to assess CCS and NET deployment under alternative CO2 prices, fossil fuel prices, electricity demand growth, and other electric sector characteristics. Final outcomes will include publicly available hourly grid operation and price data for any U.S. region of interest along with open-access capacity expansion tools for evaluating CCS/NET systems. These products will help an emerging CCS/NET industry in the United States by providing economics-driven guidance to technology developers while informing policy and investment decisions in the public and private sectors.

air capture↗

Force Limiting Vibration Tests Evaluated from both Ground Acoustic Tests and FEM Simulations of a Flight Like Vehicle System Assembly

Marshall Space Flight Center has conducted a series of ground acoustic tests with the dual goals of informing analytical judgment, and validating analytical methods when estimating vibroacoustic responses of launch vehicle subsystems. The process of repeatedly correlating finite element-simulated responses with test-measured responses has assisted in the development of best practices for modeling and post-processing. In recent work, force transducers were integrated to measure interface forces at the base of avionics box equipment. Other force data was indirectly measured using strain gauges. The combination of these direct and indirect force measurements has been used to support and illustrate the advantages of implementing the Force Limiting approach for equipment qualification tests. The comparison of force response from integrated system level tests to measurements at the same locations during component level vibration tests provides an excellent illustration. A second comparison of the measured response cases from the system level acoustic tests to finite element simulations has also produced some principles for assessing the suitability of Finite Element Models (FEMs) for making vibroacoustics estimates. The results indicate that when FEM models are employed to guide force limiting choices, they should include sufficient detail to represent the apparent mass of the system in the frequency range of interest.

Smith, Andrew↗

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗

Annual and sub-seasonal dynamics of a rapidly eroding permafrost coastline along the Beaufort Sea in northern Alaska

Drew Point, an unlithified ice-rich permafrost coastline along the Alaskan Beaufort Sea, is among the most rapidly eroding Arctic coastlines, with an average erosion rate of 19 m/yr from 2007 to 2019. We use 16 high-resolution remote sensing datasets (satellite, airborne, and UAV imagery) to analyze erosion mechanisms (thermal abrasion and denudation) in relation to environmental forcings along a 1.5 km stretch of coastline during the 2018 and 2019 open water seasons. In a striking contrast, 2019 exhibited the highest mean erosion rate (34.5 m) within the 2007–2019 record, while 2018 had the second lowest (11.2 m). Block failure contributed to sub-seasonal erosion rates 6 to 21 times higher than thermal denudation, with staggered block fall timing, lag responses post-storm, and non-storm block collapse influencing overall erosion magnitude and timing. To quantify wind effects, we developed wind sums, a metric combining cumulative wind speed and directional data that can be used as a proxy for integrated storm intensity capable of incorporating lagged responses that correlated strongly with erosion at sub-seasonal and annual scales. Our findings emphasize the dominant role of wind during periods of open water and air temperature during the thaw season in driving permafrost coastline erosion dynamics, while highlighting the importance of spatiotemporally high-resolution datasets for understanding Arctic coastal change dynamics.

Alaska Beaufort Sea Coast↗

Data-Driven Modeling and Control of Systems with Plasma-Surface Interactions (Final Technical Report)

This final technical report summarizes the activities and accomplishments in the period from February 2023 thru January 2026. The objective of the proposed research is to investigate the physical mechanisms and processes underlying the formation of structures and patterns in systems with plasma-surface interactions. In the past decades, there have been extensive studies on the interaction of glow discharges, dielectric barrier discharges, and arc discharges with confining or intervening surfaces. The advancement of the understanding of these phenomena is not only of fundamental scientific interest and relevance to the knowledge of the plasma state, but also with profound implications in various technological applications. The research will integrate theoretical, computational, and experimental work within an innovative framework of data assimilation, i.e., optimally combining model predictions with measurements. The scientific merit of this research has three aspects. Firstly, it extends the studies of plasma-surface interactions to systems with insulator surfaces and multi-layer systems, while existing studies are predominantly on electrode surfaces. Secondly, it expects to develop a novel data-driven modeling approach based on data assimilation to enhance the predictive and control capabilities, which could make transformative contributions to basic plasma research. Thirdly, it will shed new light on outstanding problems related to formation of patterns interfacing plasmas. This project also aims to launch an education and outreach initiative at Texas A&M University-Kingsville, a non-R1, minority-serving institution in South Texas. The initiative is structured as a four-tier pyramid. Tier one will be a webinar series for culture and capacity building to inform broader audience in the region about the research fields of plasma science and engineering. Tier two will be the creation and offering of an upper-level undergraduate course on introductory plasma physics, which will help with the recruitment for the upper tiers. On tier three, we will engage and mentor senior design students to conduct work toward the research goal of this project. There will also be a certificate program on general plasma science for undergrad and graduate students, part of which will be lab training at Princeton University. Tier four will be the supervision and mentoring of Ph.D. students. Therefore, this project will systematically expand the talent pipeline, broaden participation from communities historically and geographically underrepresented in DOE SC research portfolio, significantly improve the research and education capacity at the PI’s institution, and contribute to developing a diverse workforce in plasma science and engineering.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

High-contrast Thermal Infrared Spectroscopy with ALES: The 3–4 μm Spectrum of κ Andromedae b

We present the first L-band (2.8–4.1 μm) spectroscopy of κ Andromedae b, a ~20 M Jup companion orbiting at 1" projected separation from its B9-type stellar host. We combine our Large Binocular Telescope (LBT) Arizona Lenslets for Exoplanet Spectroscopy (ALES) integral field spectrograph data with measurements from other instruments to analyze the atmosphere and physical characteristics of κ And b. We report a discrepancy of ~20% (2σ) in the L' flux of κ And b when comparing to previously published values. We add an additional L' constraint using an unpublished imaging data set collected in 2013 using the LBT Interferometer/LMIRCam, the instrument in which the ALES module has been built. The LMIRCam measurement is consistent with the ALES measurement, both suggesting a fainter L-band scaling than previous studies. The data, assuming the flux scaling measured by ALES and LMIRCam imaging, are well fit by an L3-type brown dwarf. Atmospheric model fits to measurements spanning 0.9–4.8 μm reveal some tension with the predictions of evolutionary models, but the proper choice of cloud parameters can provide some relief. In particular, models with clouds extending to very low pressures composed of grains ≤1 μm appear to be necessary. If the brighter L' photometry is accurate, there is a hint that subsolar metallicity may be required.

79 ASTRONOMY AND ASTROPHYSICS↗

Pioneer 10 and 11 radio occultations by Jupiter

Results on the temperature structure of the Jovian atmosphere are reviewed which were obtained by applying an integral inversion technique combined with a model for the planet's shape based on gravity data to Pioneer 10 and 11 radio-occultation data. The technique applied to obtain temperature profiles from the Pioneer data consisted of defining a center of refraction based on a computation of the radius of curvature in the plane of refraction and the normal direction to the equipotential surface at the closest approach point of a ray. Observations performed during the Pioneer 10 entry and exit and the Pioneer 11 exit are analyzed, sources of uncertainty are identified, and representative pressure-temperature profiles are presented which clearly show a temperature inversion between 10 and 100 mb. Effects of zonal winds on the reliability of radio-occultation temperature profiles are briefly discussed.

Kliore, A. J.↗

Space simulation in the next 20 years

This is a brief overview of logically expected advancements in the area of space simulation over the next twenty years. Current NASA Space Shuttle simulations will be upgraded to support more complex payload and on-orbit tasks. This includes the ability to integrate various remote ground facilities with a real time space mission simulator and an expanded use of efficient part-task simulations. Software compatibilities between simulators will increase and each simulator will have a more combined training and engineering role. Software development processors will be increasingly internetted to an integrated data processing system.

Knorre, M. J.↗

Low-cost, Risk-Reduction Testing of Class D Spacecraft Photovoltaic Systems

The end-to-end verification of a spacecraft photovoltaic power generation system requires light! A low-cost, portable, and end-to-end photovoltaic-system test appropriate for NASAs new generation of Class D missions is presented. High risk, low-cost, and quick-turn satellites rarely have the resources to execute the traditional approaches from higher-class (A-C) missions. The Class D approach, as demonstrated on the Lunar Atmospheric and Dust Environment Explorer (LADEE), utilizes a portable, metal-halide, theatre lamp for an end-to-end photovoltaic system test. While not as precise and comprehensive as the traditional Large Area Pulsed Solar Simulator (LAPSS) test, the LADEE method leverages minimal resources into an ongoing assessment program that can be applied through numerous stages of the mission. The project takes a true Class D approach in assessing the technical value of a costly, high-fidelity performance test versus a simpler approach with less programmatic risk. The resources required are a fraction of that for a LAPSS test, and is easy to repeat due to its portability. Further, the test equipment can be handed down to future projects without building an on-site facility.At the vanguard of Class D missions, the LADEE team frequently wrestled with and challenged the status quo. The philosophy of risk avoidance at all cost, typical to Class A-C missions, simply could not be executed. This innovative and simple testing solution is contextualized to NASA Class D programs and a specific risk encountered during development of the LADEE Electrical Power System (EPS). Selection of the appropriate lamp and safety concerns are discussed, with examples of test results. Combined with the vendors panel-level data and periodic inspection, the method ensures system integrity from Integration and Test (IT) through launch. Following launch, mission operations tools are utilized to assess system performance based on a scant amount of available data.

photovoltaic↗

Simulation and Modeling of Flow Generated by Gust Generator in a Wind Tunnel

A computational study of a gust field generated by a gust generator in a low-speed wind tunnel. The gust generator is designed for the University of Washington Aeronautical Laboratory (UWAL) Kirsten wind tunnel for a gust load alleviation (GLA) control experiment of a Common Research Model (CRM) flexible wing utilizing the Variable Camber Continuous Trailing Edge Flap (VCCTEF). The gust generator comprises four horizontal NACA 0015 gust generator vanes placed upstream of the test section. Computational fluid dynamics simulations using a two-dimensional (2D) Unsteady-Reynolds-Averaged-Navier-Stokes (URANS) with k-ω Shear Stress Transport (SST) turbulence model provide detailed time-resolved information about the generated flow by the gust generator under prescribed sinusoidal motion. The characteristics of the induced flow by the gust generator are analyzed. A gust propagation model of the gust field is investigated. An unsteady lift model is developed using a varying-fidelity approach which includes a 2D interference aerodynamic model of the combined gust generator-wing system. The computed integrated unsteady lift is compared to experimental data for validation of the unsteady lift model. Both the amplitude and transport delay are found to be accurately captured by the unsteady lift model.

Nhan, Nguyen↗

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗