Search NASA⌕ Search

SEARCH · Search NASA

Results for “Reservoir Computing”

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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient Electrochemical Reduction of CO 2 to Formate in Methanol Solutions by Mn‐Functionalized Electrodes in the Presence of Amines**

Carbon cloth electrode modified by covalently attaching a manganese organometallic catalyst is used as cathode for the electrochemical reduction of CO 2 in methanol solutions. Six different industrial amines are employed as co-catalyst in millimolar concentrations to deliver a series of new reactive system. While such absorbents were so far believed to provide a CO 2 reservoir and act as sacrificial proton source, we herein demonstrate that this role can be played by methanol, and that the adduct formed between CO 2 and the amine can act as an effector or inhibitor toward the catalyst, thereby enhancing or reducing the production of formate. Pentamethyldiethylentriamine (PMDETA), identified as the best effector in our series, converts CO 2 in wet methanolic solution into bisammonium bicarbonate. Computational studies revealed that this adduct is responsible for a barrierless transformation of CO 2 to formate by the reduced form of the Mn catalyst covalently bonded to the electrode surface. As a consequence, selectivity can be switched on demand from CO to formate anion, and in the case of (PMDETA) an impressive TON HCOO- of 2.8×10 4 can be reached. This new valuable knowledge on an integrated capture and utilization system paves the way toward more efficient transformation of CO 2 into liquid fuel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

A basic code for the prediction of transient three-dimensional turbulent flowfields

A primitive pressure-velocity finite difference code has been developed to predict transient three-dimensional turbulent flow. The code is a simplified yet effective prediction procedure for use by persons with little experience in computational fluid dynamics, and into which user-oriented complexities can easily be added. The method is based on the transient two-dimensional Los Alamos SOLA prediction technique for laminar flows. Turbulence is simulated by means of a two-equation turbulence model; species diffusion and buoyancy are also included. Two applications of the code are presented to local destratification near the release structure of a reservoir and to the deflection of a jet entering normally into a uniform cross-flow. Predicted results exhibit good agreement with experimental data, showing that a useful characterization of fully three-dimensional flows is now available.

Busnaina, A. A.↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
NASA NTRS1974

Automatic classification of eutrophication of inland lakes from spacecraft data

The author has identified the following significant results. Spacecraft data and computer techniques can be used to rapidly map and store onto digital tapes watershed land use information. Software is now available by which this land use information can be rapidly and economically extracted from the tapes and related to coliform counts and other lake contaminants (e.g. phosphorus). These tools are basic elements for determining those land use factors and sources of nutrients that accelerate eutrophication in lakes and reservoirs.

Rogers, R. H.↗
DOE OSTI2021

A comprehensive techno-eco-assessment of CO 2 enhanced oil recovery projects using a machine-learning assisted workflow

Carbon dioxide enhanced oil recovery (CO 2 -EOR) projects not only extract residual oil but also sequestrate CO 2 in the depleted reservoirs. Here, this study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO 2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO 2 -EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO 2 (CO 2 -WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO 2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.

03 NATURAL GAS↗
DOE OSTI2025

Three-Dimensional Pore Networks in Miocene Stevens Sandstone of California: Implications for CO 2 Geologic Storage

The Miocene Stevens Sandstone in the San Joaquin Basin of California is increasingly recognized as a promising candidate for CO 2 geological storage due to the enormous storage capacity, proven sealing, and existing infrastructure. In this study, computed microtomography imaging and pore network modeling were employed to investigate the influence of pore geometry and wettability on the CO 2 injectivity and residual trapping. Image analysis revealed that a significant fraction of the cement and matrix consists of microporous regions. The microporosity can substantially increase the overall pore space, yet its contribution to permeability remains modest, particularly in samples with low permeability. The intrinsic heterogeneity of turbidite reservoirs further complicates the reservoir properties among different layers. Two-phase flow simulations under varying wettability conditions (water-wet, weak water-wet, and neutral-wet) demonstrated that the CO 2 injection is predominantly controlled by macropores. CO 2 invades microporous regions only after these larger pores are filled. The presence of microporosity leads to a decrease in both initial and residual CO 2 saturations, with the magnitude of the reduction being influenced by wettability. Neutral-wet scenarios exhibit higher CO 2 mobility and thus lower residual trapping than water-wet scenarios. The results imply that heterogeneity in pore geometry and cement distribution across different layers can result in stratified CO 2 flow pathways, complicating efforts to predict injection performance. Overall, the Stevens Sandstone shows considerable promise for CO 2 geologic storage, but effective implementation will require detailed characterization of the pore structure as well as the integration of reactive fluid flow to account for potential mineral dissolution and fines migration.

fluids↗
DOE OSTI2020

Coupled Time-Lapse Full-Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation

We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversion (FWI), subsurface flow processes, and rock physics models. For the inverse modeling, we handle the back propagation of gradients by an intrusive automatic differentiation strategy that offers three levels of user control: (1) At the wave physics level, we adopted the discrete adjoint method in order to use our existing high-performance FWI code; (2) at the rock physics level, we used built-in automatic differentiation operators from the TensorFlow backend; (3) at the flow physics level, we implemented customized partial differential equation (PDE) operators for the multiphase flow equations. The three-level coupled inversion strategy strikes a good balance between computational efficiency and programming efforts, and when the gradients are chained together, it constitutes a coupled inverse system. Our numerical experiments demonstrate that the three-level coupled inverse problem is superior in terms of accuracy to a traditional decoupled inversion strategy. Additionally, our method is able to simultaneously invert for parameters in empirical relationships such as the rock physics models. Our proposed inverted model can be used for reservoir performance prediction and reservoir management/optimization purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗
DOE OSTI2021

Gouge formation and dilation impacts to flow during fracture shearing

We report flow in fractures is of critical importance to fluid flow in low-permeability shales, particularly when they restrict vertical migration from reservoirs. The Mt. Simon Sandstone is a target for geologic CO 2 sequestration and the overlying Eau Claire Formation is the sealing formation capping this reservoir. The lower Eau Claire, a primary barrier to upward migration of CO 2 from the Mt. Simon, is lithologically heterogenous with a mixture of sandstone and shale. We use a series of flow-through experiments on fractured samples from the Eau Claire to document the morphological changes taking place in the fracture and surrounding rock as it is sheared in a stepwise fashion. The samples were imaged using computed tomography after each shearing episode and flow resistance was calculated based on the pressure differential across each core. We found that gouge accumulations in the fracture plane led to specific instances of self-sealing behavior despite the fact that most samples increased in average fracture aperture and transmissivity. Gouge limited the increase or, in some cases, decreased flow through these samples. Fractures adjacent to shale-rich zones and concordant with bedding were more likely to show evidence of self-sealing, while increased sandstone content and fracture orientation perpendicular to bedding planes was more likely to result in fracture dilation and fracture permeability increases. Reactivation and shearing of vertical fractures in lithologically heterogenous caprocks is more likely to pose risks to long term CO 2 storage scenarios.

58 GEOSCIENCES↗
DOE OSTI2016

The thermal regime of the Campi Flegrei magmatic system reconstructed through 3D numerical simulations

In this paper, we illustrate a quantitative conductive/convective thermal model incorporating a wide range of geophysical, petrological, geological, geochemical and isotopical observations that constrain the thermal evolution and present state of the Campi Flegrei caldera (CFc) magmatic system. The proposed model has been computed on the basis of the current knowledge of: (1) the volcanic and magmatic history of the volcano over the last 44 ka, (2) its underlying crustal structure, and (3) the physical properties of the erupted magmas. 3D numerical simulations of heat conduction and convection within heterogeneous rock/magma materials with evolving heat sources and boundary conditions that simulate magma rise from a deep (≥ 8 km depth) to shallow (2–6 km) reservoirs, magma chamber formation, magma extrusion, caldera collapse, and intra-caldera hydrothermal convection, have been carried out. The evolution of the CFc magmatic system through time has been simulated through different steps related to its changes in terms of depth, location and size of magma reservoirs and their replenishment. The thermal modeling results show that both heat conduction and convection have played an important role in the CFc thermal evolution, although with different timing. Finally, the simulated present heat distribution is in agreement with the measured geothermal profiles (Agip, 1987), reproduces the thermal gradient peaks at the CFc margins in correspondence to the anomalies in surface gradients (Corrado et al., 1998), and suggests temperatures of 700 °C at depth of 4 km in the central portion of the caldera, in agreement with the estimated temperature for the brittle-ductile transition (Hill, 1992).

3D conductive/convective model↗
DOE OSTI2021

Precipitation of dopants on acceptor-doped LaMnO3±δ revealed by defect chemistry from first principles

Perovskite oxides degrade at elevated temperatures while precipitating dopant-rich particles on the surface. A knowledge-based improvement of surface stability requires a fundamental and quantitative understanding of the dopant precipitation mechanism on these materials. We propose that dopant precipitation is a consequence of the variation of dopant solubility between calcination and operating conditions in solid oxide fuel cells (SOFCs) and electrolyzer cells (SOECs). To study dopant precipitation, we use 20% (D = Ca, Sr, Ba)-doped LaMnO3+δ (LDM20) as a model system. We employ a defect model taking input from density functional theory calculations. The defect model considers the equilibration of LDM20 with a reservoir consisting of dopant oxide (DO), peroxide (DO2), and O2 in the gas phase. The equilibrated non-stoichiometry of the A-site and B-site as a function of temperature, T, and oxygen partial pressure, p(O2), reveals three regimes for LDM20: A-site deficient (oxidizing conditions), A-site rich (atmospheric conditions), and near-stoichiometric (reducing conditions). Assuming an initial A/B non-stoichiometry, we compute the dopant precipitation boundaries in a p–T phase diagram. Our model predicts precipitation both under reducing (DO) and under highly oxidizing conditions (DO2). We found precipitation under anodic, SOEC conditions to be promoted by large dopant size, while under cathodic, SOFC conditions precipitation is promoted by initial A-site excess. The main driving forces for precipitation are oxygen uptake by the condensed phase under oxidizing conditions and oxygen release assisted by B-site vacancies under reducing conditions. Possible strategies for mitigating dopant precipitation under in electrolytic and fuel cell conditions are discussed.

Hess, Franziska (ORCID:000000024460643X)↗
DOE OSTI2024

Multi-Scale 3D Imaging for Machine Learning Property Upscaling: Mt. Simon Sandstone Case Study

Petrographic properties of principal target reservoirs for carbon sequestration, such as the Mt. Simon Sandstone, are relevant to broad interest groups. The Mt. Simon Sandstone is a deep, saline, regionally extensive Cambrian sandstone, overlain by low permeability sealing formations, making it one of the viable geologic carbon storage reservoirs in the Midwestern US. Its thickness (exceeding 2400 ft in some localities), depth, and lateral extent, combined with high porosity and permeability make it a high-priority target of multiple ongoing geologic carbon sequestration efforts in the United States of America. The National Energy Technology Laboratory in Morgantown, West Virginia, has been engaged in characterization efforts of the Mt. Simon for over a decade, with a strong focus on Computed Tomographic data acquisition. Data generated during this period has been hitherto not accessible to the public. This archival effort focused on preservation of historical CT data and associated metadata, and facilitating their accessibility, culminating with the publication of the entire dataset on NETL’s Energy Data eXchange (EDX) and the associated Gill et. al (2024) paper.

Gill, Magdalena K.↗
DOE OSTI2023

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

58 GEOSCIENCES↗
DOE OSTI2015

HYDRATE v1.5 OPTION OF TOUGH+ v1.5

HYDRATE v1.5 is a numerical code that for the simulation of the behavior of hydrate-bearing geologic systems, and represents the third update of the code since its first release [Moridis et al., 2008]. It is an option of TOUGH+ v1.5 [Moridis and Pruess, 2014], a successor to the TOUGH2 [Pruess et al., 1999, 2012] family of codes for multi-component, multiphase fluid and heat flow developed at the Lawrence Berkeley National Laboratory. HYDRATE v1.5 needs the TOUGH+ v1.5 core code in order to compile and execute. It is written in standard FORTRAN 95/2003, and can be run on any computational platform (workstation, PC, Macintosh) for which such compilers are available. By solving the coupled equations of mass and heat balance, the fully operational TOUGH+HYDRATE code can model the non-isothermal gas release, phase behavior and flow of fluids and heat under conditions typical of common natural CH4-hydrate deposits (i.e., in the permafrost and in deep ocean sediments) in complex geological media at any scale (from laboratory to reservoir) at which Darcy's law is valid. TOUGH+HYDRATE v1.5 includes both an equilibrium and a kinetic model of hydrate formation and dissociation. The model accounts for heat and up to four mass components, i.e., water, CH4, hydrate, and water-soluble inhibitors such as salts or alcohols. These are partitioned among four possible phases (gas phase, liquid phase, ice phase and hydrate phase). Hydrate dissociation or formation, phase changes and the corresponding thermal effects are fully described, as are the effects of inhibitors. The model can describe all possible hydrate dissociation mechanisms, i.e., depressurization, thermal stimulation, salting-out effects and inhibitor-induced effects.

Moridis, GeorgeJ↗
DOE OSTI2018

Experimental Investigation of Gas Flow and Hydrate Formation Within the Hydrate Stability Zone

Here, we form methane hydrate by injecting methane gas into a brine-saturated, coarse-grained sample under hydrate-stable thermodynamic conditions. Hydrate forms to a saturation of 11%, which is much lower than that predicted assuming three-phase (gas-hydrate-brine) thermodynamic equilibrium (67%). During hydrate formation, there are temporary flow blockages. We interpret that a hydrate skin forms a physical barrier at the gas-brine interface. The skin fails periodically when the pressure differential exceeds the skin strength. Once the skin is present, further hydrate formation is limited by the rate that methane can diffuse through the solid skin. This process produces distinct thermodynamic states on either side of the skin that allows gas to flow through the sample. This study illuminates how gas can be transported through the hydrate stability zone and thus provides a mechanism for the formation of concentrated hydrate deposits in sand reservoirs. It also illustrates that models that assume local equilibrium at the core-scale and larger may not capture the fundamental behaviors of these gas flow and hydrate formation processes.

58 GEOSCIENCES↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
DOE OSTI2017

A risk map methodology to assess the spatial and temporal distribution of leakage into groundwater from Geologic Carbon Storage

The risks to potable aquifers due to brine leakage through plugged and abandoned (P&A) wells is highly uncertain and a potentially significant contributor to the risk profile in Geologic Carbon Storage (GCS). This uncertainty stems from the unknown location of wells and the large variance of P&A wellbore permeability, making the spatial assessment of P&A brine leakage risk challenging. A new methodology is presented to generate “risk maps”, or spatial distributions of brine leakage risk to groundwater resources as defined with no-impact or Maximum Contaminant Level (MCL) thresholds. The methodology utilizes probability theory, thereby avoiding the use of computationally expensive Monte Carlo simulations while maintaining flexibility in modeling techniques. These maps provide quantitative probabilities of risk as a function of time to inform site selection and monitoring during and post-injection, conducive to the US EPA's permitting of class-VI wells and the so-called “area of review”, AoR. As a demonstration of the methodology, a numerical model of a hypothetical fully-coupled system spanning from the injection reservoir to the USDW is used to assess the evolution of brine leakage through P&A wells. Risk maps of CO 2 leakage can also be generated with this methodology for a comprehensive assessment of GCS leakage risk.

58 GEOSCIENCES↗