Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Co”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effects of surface diffusion in electrocatalytic CO 2 reduction on Cu revealed by kinetic Monte Carlo simulations

We report Kinetic Monte Carlo (KMC) methods are frequently used for mechanistic studies of thermally driven heterogeneous catalysis systems but are underused for electrocatalysis. Here, we develop a lattice KMC approach for electrocatalytic CO 2 reduction. The work is motivated by a prior experimental report that performed electroreduction of a mixed feed of 12 CO 2 and 13 CO on Cu; differences in the 13 C content of C2 products ethylene and ethanol (Δ 13 C) were interpreted as evidence of site selectivity. The lattice KMC model considers the effect of surface diffusion on this system. In the limit of infinitely fast diffusion (mean-field approximation), the key intermediates 12 CO* and 13 CO* would be well mixed on the surface and no evidence of site selectivity could have been observed. Using a simple two-site model and adapting a previously reported microkinetic model, we assess the effects of diffusion on the relative isotope fractions in the products using the estimated surface diffusion rate of CO* from literature reports. We find that the size of the active sites and the total surface adsorbate coverage can have a large influence on the values of Δ 13 C that can be observed. Δ 13 C is less sensitive to the CO* diffusion rate as long as it is within the estimated range. We further offer possible methods to estimate surface distribution of intermediates and to predict intrinsic selectivity of active sites based on experimental observations. This work illustrates the importance of considering surface diffusion in the study of electrochemical CO 2 reduction to multi-carbon products. Our approach is entirely based on a freely available open-source code, so will be readily adaptable to other electrocatalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Action at a distance: The remarkable coupling of CO 2 uptake to electron transfer in specialized cyanobacterial NDH-1 complexes

Cyanobacteria achieve highly efficient photosynthesis using a CO 2 -concentrating mechanism relying on specialized Type I (NDH-1) complexes. Among these, NDH-1 3 and NDH-1 4 catalyze redox-coupled hydration of CO 2 to bicarbonate, supporting carbon fixation in carboxysomes. The mechanism of coupling electron transfer to CO 2 -hydration by these variant NDH-1 complexes remains unknown. We engineered a Synechococcus PCC7942 strain that expresses exclusively the high flux/low affinity NDH-14 complex, enabling the observation of the coupling of CO 2 hydration to cyclic electron flow in isolation from the other NDH-1 isoforms normally present in cells. We found that inhibition of the CupB protein by the carbonic anhydrase inhibitor ethoxzolamide (EZ) suppressed CO 2 uptake, slowed photosystem I rereduction, and abolished proton pumping as probed by acridine orange fluorescence. These effects were absent in strains lacking Cup proteins, confirming specificity. The results demonstrate that CO 2 hydration and electron transfer through NDH-14 are tightly coupled via proton translocation across the thylakoid membrane. These findings provide direct evidence for the bidirectional interaction in bioenergetic coupling between the plastoquinone reduction and the CO 2 uptake at the distal Zn-site over a span of ~150 Å and support a proton-removal hypothesis involving the proton transfer pathways from the Zn-site of CO2 hydration to an energetically coupled proton loading site evolutionarily repurposed from the ancestral proton pumping mechanism to enable energetic CO 2 uptake.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Overestimated natural biological nitrogen fixation translates to an exaggerated CO 2 fertilization effect in Earth system models

CO 2 fertilization of the terrestrial biosphere is limited by nitrogen. Biological nitrogen fixation (BNF) is the dominant natural nitrogen source to the terrestrial biosphere and can alleviate nitrogen limitation but is poorly constrained in Earth system models (ESMs). Here, in this study, we compare terrestrial BNF from an ensemble of ESMs of the 6th Coupled Model Intercomparison Project to a new global synthesis of observations across natural and agricultural biomes. We find that compared to observations, ESMs underestimate agricultural BNF but overestimate natural BNF in the present day by over 50%. Natural BNF is overestimated in the most productive ecosystems that contribute most to the terrestrial carbon sink (forests and grasslands). ESMs with different BNF representations yield a range of BNF responses to CO 2 enrichment. Some ESMs with phenomenological representations of BNF predict a natural BNF increase in response to a doubling of CO 2 that aligns with a meta-analysis of CO 2 enrichment experiments (31% increase) but fail to account for the substantial carbon cost of BNF. In contrast, ESMs with mechanistic representations of BNF account for its carbon cost as well as its regulation by nitrogen limitation but overestimate the BNF response to a doubling of CO 2 (135% increase). Overall, all current BNF representations in ESMs fall short of fully capturing its response to rising atmospheric CO 2 . Finally, we find a positive correlation between modeled present-day natural BNF and the CO 2 fertilization effect across ESMs, suggesting that overestimated natural BNF translates to an exaggerated CO 2 fertilization effect of approximately 11% in ESMs.

Biological nitrogen fixation↗
DOE OSTI2023

High-pressure CO 2 dissociation with nanosecond pulsed discharges

Herein we investigate the conversion of CO 2 into CO and O 2 with nanosecond repetitively pulsed (NRP) discharges in a high-pressure batch reactor. Stable discharges are obtained at up to 12 bar. By-products are measured with gas chromatography. The energy efficiency is determined for a range of processing times, pulse energy, and fill pressures. It is only weakly sensitive to the plasma operating parameters, i.e the extent of CO 2 conversion is almost linearly-dependent on the specific energy invested. A conversion rate as high as 14% is achieved with an energy efficiency of 23%. For long processing times, saturation in the yield and a drop in efficiency are observed, due to the increasing role of three-body recombination reactions, as described by zero-dimensional detailed kinetic modeling. The modeling reveals the presence of three-stage kinetics between NRP pulses, controlled by electron-impact CO 2 dissociation, vibrational relaxation, and neutral elementary kinetics. Transport effects are shown to be important for CO 2 conversion at high pressures. For fill pressures beyond 10 bar, CO 2 may locally transit into supercritical states. The supercritical plasma kinetics may bypass atomic oxygen pathways and directly convert CO 2 into O 2 . This work provides a detailed analysis of plasma-based high-pressure CO 2 conversion, which is of great relevance to future large-scale sustainable carbon capture, utilization, and storage.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

First simultaneous observation of co- and counter-current fast-ion losses in the ASDEX Upgrade tokamak

In ITER and future fusion power plants, the source of the fusion born alpha particles is almost isotropic in pitch angle, thus having co- and counter-current populations. For trapped ions, the co-current side of the orbit corresponds to its outer leg, while the counter-current side corresponds to the inner leg. Understanding the mechanisms responsible for the fast-ion losses (FILs) is critical for future magnetically confined fusion power plants. To further study the interplay of fast ions with plasma instabilities, a double pinhole collimator has been developed for a Fast-Ion Loss Detector (FILD) in the ASDEX Upgrade tokamak (AUG). This new FILD opens the operational window to simultaneous measurements of the co- and counter-current ion velocity-space. In this paper, the first results for the AUG double collimator FILD detector are shown. The commissioning of this new probe is carried out in H-mode plasmas with an on-axis magnetic field $B_0 = -2.5$ T, and a plasma current $I_{\textrm{p}} = 0.7$ MA. Simultaneous co- and counter-current FILs have been measured. Both have shown a similar dependence on Ion Cyclotron Resonance Heating (ICRH) power, where the main difference is the intensity of the losses, with the co-losses being an order of magnitude larger. Toroidal Alfvén eigenmode-coherent ICRH-only losses have been identified for the co-current ions. Additionally, the presence of Edge Localized Modes during the discharge were shown to increment Neutral Beam Injection prompt losses, while partially mitigating ICRH-driven losses on both co- and counter- sides of the velocity-space. Finally, a very trapped and high gyroradius losses, with an unclear origin, have been measured in the co- and counter-current velocity-space. The computed ion trajectories show that these ions remain permanently near the vessel wall, suggesting that they are accelerated within the scrape-off layer.

ELM↗
DOE OSTI2021

Maximum CO 2 diffusion inside leaves is limited by the scaling of cell size and genome size

Maintaining high rates of photosynthesis in leaves requires efficient movement of CO 2 from the atmosphere to the mesophyll cells inside the leaf where CO 2 is converted into sugar. CO 2 diffusion inside the leaf depends directly on the structure of the mesophyll cells and their surrounding airspace, which have been difficult to characterize because of their inherently three-dimensional organization. Yet faster CO 2 diffusion inside the leaf was probably critical in elevating rates of photosynthesis that occurred among angiosperm lineages. Here we characterize the three-dimensional surface area of the leaf mesophyll across vascular plants. We show that genome size determines the sizes and packing densities of cells in all leaf tissues and that smaller cells enable more mesophyll surface area to be packed into the leaf volume, facilitating higher CO 2 diffusion. Measurements and modelling revealed that the spongy mesophyll layer better facilitates gaseous phase diffusion while the palisade mesophyll layer better facilitates liquid-phase diffusion. Our results demonstrate that genome downsizing among the angiosperms was critical to restructuring the entire pathway of CO 2 diffusion into and through the leaf, maintaining high rates of CO 2 supply to the leaf mesophyll despite declining atmospheric CO 2 levels during the Cretaceous.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2022

Deep learning multiphysics network for imaging CO 2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. Here we use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2021

Statistical upscaling of ecosystem CO 2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties

Abstract The regional variability in tundra and boreal carbon dioxide (CO 2 ) fluxes can be high, complicating efforts to quantify sink‐source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO 2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO 2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high‐latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO 2 fluxes and test the accuracy and uncertainty of different statistical models. CO 2 fluxes were upscaled at relatively high spatial resolution (1 km 2 ) across the high‐latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE‐focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO 2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m −2 yr −1 , respectively) compared to tundra (average annual NEE +10 and −2 g C m −2 yr −1 ). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO 2 budget, estimated using the annual NEE ensemble prediction, suggests the high‐latitude region was on average an annual CO 2 sink during 1990–2015, although uncertainty remains high.

Virkkala, Anna‐Maria↗
DOE OSTI2021

Warming and elevated CO 2 promote rapid incorporation and degradation of plant‐derived organic matter in an ombrotrophic peatland

Abstract Rising temperatures have the potential to directly affect carbon cycling in peatlands by enhancing organic matter (OM) decomposition, contributing to the release of CO 2 and CH 4 to the atmosphere. In turn, increasing atmospheric CO 2 concentration may stimulate photosynthesis, potentially increasing plant litter inputs belowground and transferring carbon from the atmosphere into terrestrial ecosystems. Key questions remain about the magnitude and rate of these interacting and opposing environmental change drivers. Here, we assess the incorporation and degradation of plant‐ and microbe‐derived OM in an ombrotrophic peatland after 4 years of whole‐ecosystem warming (+0, +2.25, +4.5, +6.75 and +9°C) and two years of elevated CO 2 manipulation (500 ppm above ambient). We show that OM molecular composition was substantially altered in the aerobic acrotelm, highlighting the sensitivity of acrotelm carbon to rising temperatures and atmospheric CO 2 concentration. While warming accelerated OM decomposition under ambient CO 2 , new carbon incorporation into peat increased in warming × elevated CO 2 treatments for both plant‐ and microbe‐derived OM. Using the isotopic signature of the applied CO 2 enrichment as a label for recently photosynthesized OM, our data demonstrate that new plant inputs have been rapidly incorporated into peat carbon. Our results suggest that under current hydrological conditions, rising temperatures and atmospheric CO 2 levels will likely offset each other in boreal peatlands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Trading water for carbon in the future: Effects of elevated CO 2 and warming on leaf hydraulic traits in a semiarid grassland

Abstract The effects of climate change on plants and ecosystems are mediated by plant hydraulic traits, including interspecific and intraspecific variability of trait phenotypes. Yet, integrative and realistic studies of hydraulic traits and climate change are rare. In a semiarid grassland, we assessed the response of several plant hydraulic traits to elevated CO 2 (+200 ppm) and warming (+1.5 to 3°C; day to night). For leaves of five dominant species (three graminoids and two forbs), and in replicated plots exposed to 7 years of elevated CO 2 , warming, or ambient climate, we measured: stomatal density and size, xylem vessel size, turgor loss point, and water potential (pre‐dawn). Interspecific differences in hydraulic traits were larger than intraspecific shifts induced by elevated CO 2 and/or warming. Effects of elevated CO 2 were greater than effects of warming, and interactions between treatments were weak or not detected. The forbs showed little phenotypic plasticity. The graminoids had leaf water potentials and turgor loss points that were 10% to 50% less negative under elevated CO 2 ; thus, climate change might cause these species to adjust their drought resistance strategy away from tolerance and toward avoidance. The C4 grass also reduced allocation of leaf area to stomata under elevated CO 2 , which helps explain observations of higher soil moisture. The shifts in hydraulic traits under elevated CO 2 were not, however, simply due to higher soil moisture. Integration of our results with others' indicates that common species in this grassland are more likely to adjust stomatal aperture in response to near‐term climate change, rather than anatomical traits; this contrasts with apparent effects of changing CO 2 on plant anatomy over evolutionary time. Future studies should assess how plant responses to drought may be constrained by the apparent shift from tolerance (via low turgor loss point) to avoidance (via stomatal regulation and/or access to deeper soil moisture).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Photosynthetic responses of switchgrass to light and CO 2 under different precipitation treatments

Switchgrass ( Panicum virgatum L .) is a prominent bioenergy crop with robust resilience to environmental stresses. However, our knowledge regarding how precipitation changes affect switchgrass photosynthesis and its responses to light and CO 2 remains limited. To address this knowledge gap, we conducted a field precipitation experiment with five different treatments, including −50%, −33%, 0%, +33%, and +50% of ambient precipitation. To determine the responses of leaf photosynthesis to CO 2 concentration and light, we measured leaf net photosynthesis of switchgrass under different CO 2 concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO 2 response models (i.e., rectangular hyperbola model, nonrectangular hyperbola model, exponential model, and the modified rectangular hyperbola model) using the measurements in the ambient precipitation treatment. Based on the fitting criteria, we selected the nonrectangular hyperbola model as the optimal model and applied it to all precipitation treatments, and estimated model parameters. Overall, the model fit field measurements well for the light and CO 2 response curves. Precipitation change did not influence the maximum net photosynthetic rate ( P max ) but influenced other model parameters including quantum yield ( α ), convexity ( θ ), dark respiration ( Rd ), light compensation point ( LCP ), and saturated light point ( LSP ). Specifically, the mean P max of five precipitation treatments was 17.6 μmol CO 2 m −2 s −1 , and the ambient treatment tended to have a higher P max . The +33% treatment had the highest α , and the ambient treatment had lower θ and LCP , higher Rd , and relatively lower LSP . Furthermore, precipitation significantly influenced all model parameters of CO 2 response. The ambient treatment had the highest P max , largest α , and lowest θ , R d , and CO 2 compensation point LCP . Overall, this study improved our understanding of how switchgrass leaf photosynthesis responds to diverse environmental factors, providing valuable insights for accurately modeling switchgrass ecophysiology and productivity.

09 BIOMASS FUELS↗
DOE OSTI2022

Accelerated sea-level rise is suppressing CO 2 stimulation of tidal marsh productivity: A 33-year study

Accelerating relative sea-level rise (RSLR) is threatening coastal wetlands. However, rising CO 2 concentrations may also stimulate carbon sequestration and vertical accretion, counterbalancing RSLR. A coastal wetland dominated by a C 3 plant species was exposed to ambient and elevated levels of CO 2 in situ from 1987 to 2019 during which time ambient CO 2 concentration increased 18% and sea level rose 23 cm. Plant production did not increase in response to gradually rising ambient CO 2 concentration during this period. Elevated CO 2 increased shoot production relative to ambient CO 2 for the first two decades, but from 2005 to 2019, elevated CO 2 stimulation of production was diminished. The decline coincided with increases in relative sea level above a threshold that hindered root productivity. While elevated CO 2 stimulation of elevation gain has the potential to moderate the negative impacts of RSLR on tidal wetland productivity, benefits for coastal wetland resilience will diminish in the long term as rates of RSLR accelerate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Skin CO 2 sniffing for wearable metabolic monitoring

CO 2 is a key physiological parameter used to assess hypoventilation and to estimate metabolic rates. However, current CO 2 monitoring relies on bulky breath-analysis systems that are impractical for continuous use in daily life. Here, we present a wearable on-skin gas-sniffing system that quantifies skin-emitted CO 2 and establishes its physiological relevance through direct correlation with exhaled CO 2 and metabolic rate. Participant studies demonstrate strong correlations between CO 2 flow rates from the skin and breath during both rest and exercise, with skin-emitted CO 2 approximately four orders of magnitude lower than exhaled CO 2 . Skin-emitted CO 2 also correlates with metabolic rate, suggesting its potential as a surrogate for breath-based indirect calorimetry. With its wearable form factor and physiological relevance, this skin gas sniffing system enables continuous, noninvasive metabolic monitoring and opens opportunities for studying skin gas exchange.

Kim, Seung-Rok [University of California, Berkeley↗
DOE OSTI2025

The Impact of Membrane Inactive Area on the Durability of Pt-Co Catalyst

Platinum (Pt) is the most active catalyst for oxygen reduction reaction; however, its activity still requires a significant increase to meet the demands of practical applications. To enhance the catalytic activity, alloy catalysts like platinum-cobalt (Pt-Co), are being extensively investigated. However, Co leaching from the Pt-Co alloy remains a significant concern. Evaluating the durability of Pt-Co alloy catalyst is further complicated by variations in Co leaching, which affects both the observed performance and durability. This variability often arises from an overlooked factor: choice of the inactive area of the membrane electrode assembly (MEA) used during the evaluation. This study examines the critical role of membrane inactive area on the performance loss observed during durability testing of Pt-Co alloys. Our findings indicate that a large membrane inactive area reduces the impact of Co leaching on performance and durability, up to 200 mA cm −2 difference in performance is observed between large and small inactive area MEA at 0.7 V for dry conditions, and more Co is retained in the active area of MEA for smaller inactive areas which is responsible for larger performance losses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2020

User’s Manual for StrmtbFlow, the Stream Tube Multiphase Flow Part of the FE/NETL CO 2 Prophet Model, Version 2

The United States (U.S.) Department of Energy (DOE) Office of Fossil Energy (FE) at the National Energy Technology Laboratory (NETL) has developed the FE/NETL CO 2 Prophet Model, which is an updated version of the CO 2 Prophet. This document is a user’s manual for StrmtbFlow, which is one part of the FE/NETL CO 2 Prophet Model. CO 2 Prophet was originally developed in the 1990s by Texaco Exploration and Production Technology Department for DOE. The FE/NETL CO 2 Prophet Model is an oil reservoir simulator that is suitable for simulating water floods and supercritical carbon dioxide (CO 2 ) enhanced oil recovery (EOR). The FE/NETL CO 2 Prophet Model uses a number of assumptions to simplify the equations describing the flow of oil, water (or brine), and CO 2 in the oil reservoir.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

User’s Manual for StrmtbGen, the Stream Tube Generating Part of the FE/NETL CO 2 Prophet Model, Version 2

The United States (U.S.) Department of Energy (DOE) Office of Fossil Energy (FE) at the National Energy Technology Laboratory (NETL) has developed the FE/NETL CO 2 Prophet Model, which is Version 2 of CO 2 Prophet. This document is a User’s Manual for StrmtbGen, which is one part of the FE/NETL CO 2 Prophet Model. CO 2 Prophet was originally developed in the 1990s by Texaco Exploration and Production Technology Department for DOE. The FE/NETL CO 2 Prophet Model is an oil reservoir simulator that is suitable for simulating water floods and supercritical carbon dioxide (CO 2 ) enhanced oil recovery (EOR). The FE/NETL CO 2 Prophet Model uses a number of assumptions to simplify the equations describing the flow of oil, water (or brine), and CO 2 in the oil reservoir.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗