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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impacts of Different Operation Conditions and Geological Formation Characteristics on CO2 Sequestration in Citronelle Dome, Alabama

Major concerns of carbon dioxide (CO2) sequestration in subsurface formations are knowledge of the well injectivity and gas storage capacity of the formation, the CO2 pressure and saturation plume extensions during and after injection, and the risks associated with CO2 leakage and fault reactivation. Saline reservoirs are considered as one of the target formations for CO2 sequestration through structural, residual, dissolution, and mineral trapping mechanisms. The boundary condition of the saline reservoir dictates the pressure and saturation plume extension of the injected supercritical CO2 that could expand over large distances. This can lead to sources of risk, e.g., leakage and/or fault reactivation due to presence of wells, thief zones, and geological discontinuities. Therefore, there is a critical need to develop a model that describes how risk-related performance metrics (i.e., the CO2 saturation plume size, the pressure differential plume area, and the pressure differential at specific locations) vary as a function of the size of injection, time following injection, injection operations, and geologic environment. In this study, a systematic reservoir modeling studies of anthropogenic CO2 sequestration in Citronelle dome, Alabama, was performed where all relevant scenarios and conditions to address the questions of the saturation and pressure plume size in the area of review (AoR) and post-injection site care (PISC) are considered. The objective for this study was firstly to systematically simulate CO2 sequestration, i.e., saturation dynamics, and pressure behavior over a range of operational and geological conditions and to derive conclusions about the factors influencing saturation and pressure plume size, post-injection behavior, and the risk associated with them, by developing third-generation reduced order models (ROMs) for reservoir behavior. Finally, to assess the uncertainty associated with our studies, Latin Hypercube Sampling (LHS) together with an experimental design technique, i.e., Plackett–Burman design, was used. Application of Pareto charts and respond surfaces enabled us to determine the most important parameters impacting saturation and pressure plume sizes and to quantify the auto- and cross-correlation among different parameters in both history-matched and upscaled models.

58 GEOSCIENCES↗
DOE OSTI2021

Numerical Modeling of CO2 Sequestration within a Five-Spot Well Pattern in the Morrow B Sandstone of the Farnsworth Hydrocarbon Field: Comparison of the TOUGHREACT, STOMP-EOR, and GEM Simulators

The objectives of this study were (1) to assess the fate and impact of CO2 injected into the Morrow B Sandstone in the Farnsworth Unit (FWU) through numerical non-isothermal reactive transport modeling, and (2) to compare the performance of three major reactive solute transport simulators, TOUGHREACT, STOMP-EOR, and GEM, under the same input conditions. The models were based on a quarter of a five-spot well pattern where CO2 was injected on a water-alternating-gas schedule for the first 25 years of the 1000 year simulation. The reservoir pore fluid consisted of water with or without petroleum. The results of the models have numerous broad similarities, such as the pattern of reservoir cooling caused by the injected fluids, a large initial pH drop followed by gradual pH neutralization, the long-term persistence of an immiscible CO2 gas phase, the continuous dissolution of calcite, very small decreases in porosity, and the increasing importance over time of carbonate mineral CO2 sequestration. The models differed in their predicted fluid pressure evolutions; amounts of mineral precipitation and dissolution; and distribution of CO2 among immiscible gas, petroleum, formation water, and carbonate minerals. The results of the study show the usefulness of numerical simulations in identifying broad patterns of behavior associated with CO2 injection, but also point to significant uncertainties in the numerical values of many model output parameters.

STOMP↗
NASA NTRSDate not supplied

Multifunctional Surface Treatment for Self-Sterilization and CO2 Sequestration for Sustainable Space Exploration

A long-term human space presence is dependent on maintaining a safe living environment. Disinfecting surfaces is essential to protecting astronauts from potential pathogens, as well as structural components of the habitat from damaging biofilms. Developing a surface coating that utilizes readily available light has the potential to provide sterilization with additional functionality. Titanium dioxide (TiO2) is a commonly used photocatalyst owing to its efficiency, economic feasibility, and safety to humans. TiO2 photocatalyst modified with other metals such as copper or iron could allow for self-sterilization, photocatalytic memory, and photoreduction of carbon dioxide (CO2) to fuels on Mars. Sustainable design of the photocatalyst should account for the resources available, specifically within the Martian and Lunar regolith. This project reviews the current technologies to design a TiO2 photocatalyst which can be incorporated into a space habitat’s inner lining.

Human Space Exploration↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
DOE OSTI2024

Reservoir rocks interacting with CO2/brine under subsurface CO2 sequestration conditions

The Cedar Keys/Lawson formation in the U.S. is considered as a potential candidate host reservoir for carbon storage. Reporting the knowledge of geochemically induced changes to the permeability and porosity of host CO2 storage sandstone will enable us to gain a deeper insight of the long-term reservoir behavior under the CO2 storage conditions. This study suggests that mineral dissolution and mineral precipitation could occur in the host deposit altering its characteristics for CO2 storage over time.

Soong, Yee↗
DOE OSTI2024

Reservoir rocks interacting with CO2 /brine under CO2 sequestration conditions

The Cedar Keys/Lawson formation in the U.S. is considered as a potential candidate host reservoir for carbon storage. Reporting the knowledge of geochemically induced changes to the permeability and porosity of host CO2 storage sandstone will enable us to gain a deeper insight of the long-term reservoir behavior under the CO2 storage conditions.

Soong, Yee↗
NASA NTRS2010

Offshore Membrane Enclosures for Growing Algae (OMEGA: A System for Biofuel Production, Wastewater Treatment, and CO2 Sequestration

We are developing Offshore Membrane Enclosures for Growing Algae (OMEGA). OMEGAs are closed photo-bioreactors constructed of flexible, inexpensive, and durable plastic with small sections of semi-permeable membranes for gas exchange and forward osmosis (FO). Each OMEGA modules is filled with municipal wastewater and provided with CO2 from coastal CO2 sources. The OMEGA modules float just below the surface, and the surrounding seawater provides structural support, temperature control, and mixing for the freshwater algae cultures inside. The salinit7 gradient from inside to outside drives forward osmosis through the patches of FO membranes. This concentrates nutrients in the wastewater, which enhances algal growth, and slowly dewaters the algae, which facilitates harvesting. Thy concentrated algal biomass is harvested for producing biofuels and fertilizer. OMEGA system cleans the wastewater released into the surrounding coastal waters and functions as a carbon sequestration system.

Trent, Jonathan↗