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Synthetic mesoporous silica nanoparticle (MSN) is an architected geomaterial that provides confinement-mediated flow and transport properties of fluids needed for environmental research such as subsurface energy storage or carbon capture.
EOS7MG is a new TOUGH3 module for modeling water, brine, and multiple gas mixtures in the subsurface multiphase flow system. The module allows mixture of as more as three gas components selecting from a list of predefined gases (CH4, C2H6, C3H8, H2S, CO2, N2, NO2, H2, and AIR). EOS7MG was developed based on EOS7C and inherited the general modeling capabilities of the TOUGH3 platform. It uses cubic equation of states for gas density, viscosity, and enthalpy calculation. The module provides the selection options for Peng-Robinson, Redlich-Kwong, or Soave-Redlich-Kwong equations of state. The subsurface flow and transport of gas and aqueous phase mixtures over a wide range of pressures and temperatures is modeled with a multiphase Darcy's Law and an accurate chemical equilibrium approach as originally implemented in EOS7C is used for calculation of the solubilities of different mass components. EOS7MG was designed to allow flexible number of components based on the number of gases included in the model. The potential application of this module may include subsurface gas storage, energy storage, CCS, enhanced gas recovery, and more.
In conventional wellbore-integrity analysis, the cement sheath’s initial state of stress and transient thermoporoelastic effects are often neglected. However, the initial state of stress is prerequisite information for accurately predicting the safe operating conditions that prevent a cemented well from being damaged. In addition, transient thermoporoelastic effects can have a profound effect on when damage will occur. Here in this paper, we propose a model that includes these effects to predict the safe operating pressures and temperatures that will prevent cement-sheath failure. For the initial state of stress, we proposed an empirical model using measurements. Subsequent stress changes are evaluated by a fully coupled transient thermoporoelastic model to analyze the mechanical behavior of the cement sheath. We predict the safe operating envelope (SOE) for shear, tensile, and debonding cement-sheath failures caused by pressure and temperature perturbations after the cement sets. Our model predicts that pore pressure is a key factor for cement failure, especially for rapid temperature changes. If the formation is low permeability, the transient pore pressures are amplified, causing the risk of damage to increase. Compared with conventional thermoelastic models, the thermoporoelastic model predicts a smaller SOE when heating the internal casing fluid and a larger envelope when cooling the internal casing fluid. Finally, the heating rate was considered with respect to field applications. The heating rate was also considered, and slower heating/cooling rates can prevent damage to the cement sheath. Finally, the thermoporoelastic model was applied to explain several laboratory and field experiments and achieved good matches.
Estimating the distance from the hydraulic fracture tip to the monitor well can be useful for fracture characterization, well spacing optimization, and preventing parent-child well interference. A heart-shaped signal is referred to as the extensional precursor of a fracture hit recorded by crosswell strain measurements and can serve as a vital tool for such estimation. This study incorporates the 3D displacement discontinuity method (DDM) to understand the impact of fracture geometry and monitor well offset on the heart-shaped signal’s characteristics. Results from numerical simulation and analytical solutions reveal a strong linear correlation between the spatial extent of the heart-shaped signal and the fracture tip distance. This relationship was further developed to predict tip distance using field data from the Hydraulic Fracture Test Site 2 (HFTS2). A reasonable approximation result from field data further validates the methodology. In addition, it is worth noting that the estimation accuracy depends on the ratio between fracture dimension and tip distance. The findings of this study offer a novel approach for real-time monitoring and characterizing hydraulic fracture propagation, which can be further used for well spacing optimization in unconventional and enhanced geothermal system reservoir development, as well as caprock integrity monitoring for carbon sequestration projects.
Submeter-scale geologic heterogeneity greatly affects CO 2 plume migration and retention. In this work, we present meter-scale laboratory experiments that can capture the impact of realistic submeter-scale geologic heterogeneity on multiphase flow and trapping. We produce realistic sedimentary formations consisting of ripple deposits with varying grain size contrast and wettability in a meter-scale slab chamber. Then, we conduct multiphase flow experiments with analog fluids through these structures and measure the saturation patterns, capillary heterogeneity trapping (CHT), and overall trapping performance. When we alter the ripple bedform architecture, variations in trapped saturation and CHT (10–20%) increment are exhibited. Similar growth in trapping performance is also observed when grain size contrast increases. Finally, wettability changes (water- to oil-wet) can increase nonwetting saturation and CHT up to 5% and 10–20%, respectively. These results emphasize the importance of correctly characterizing the impact of small-scale heterogeneities and wettability changes. We believe this is the first time that multiphase flow experiments were conducted in meter-scale domains with realistic ripple bedforms and heterogeneous wettability to investigate plume migration and trapping.
Geological carbon sequestration (GCS) will play a critical role in decarbonization and in facilitating the transition to clean energy systems. Because CO 2 is highly mobile, ensuring its safe and permanent injection into subsurface geological formations involves monitoring over larger spatial domains and longer time periods than is typical for hydrocarbon reservoirs. This can benefit from simulation tools capable of modeling key CO 2 trapping mechanisms, particularly those optimized for speed and scalability on high-performance computing systems. Using isothermal versions of the SPE11B and SPE11C benchmark cases, we conduct a mesh refinement study simulating CO 2 injection into kilometer-scale rock formations at centimeter resolution with the GEOS open-source simulation framework. We focus on how mesh refinement improves the accuracy of convective mixing in both 2D and 3D simulations. The computational costs associated with achieving a converged solution highlight the need for predictive upscaling techniques. A systematic performance scaling analysis—including both central processing unit (CPU) and graphics processing unit (GPU) architectures—complements the “Results” section.
Global efforts to decrease carbon dioxide (CO 2 ) emissions will result in increased reliance on renewable energy sources. One of the most significant obstacles to implementing this transition is the need for energy storage to compensate for varying, and sometimes intermittent, production rates and ever-increasing demand. An attractive solution is to use surplus energy to produce hydrogen gas (H 2 ), which can then be used to produce energy on demand without emitting CO 2 . This approach would require a vast H 2 network linking generators with consumers via transmission systems. Additionally, H 2 storage would be needed to support immediate needs, but larger storage reserves also will be required to ensure energy reliability and mitigate the impact of the mismatch between variable production rates and demand. Large-scale storage of H 2 may be achieved by using underground resources similar to how natural gas (NG) has been stored for the past century. While there is much experience in underground NG storage (UGS), significant technical challenges must be addressed to economically and safely store H 2 at the commercial scale. The purpose of this report is to assess the current state of knowledge and to identify technical research needs to facilitate deployment of large-scale underground hydrogen storage (UHS).
Identification of sources for domestic critical minerals and rare earth elements (CM/REE) has been deemed essential for the energy transition by the United States Department of Energy (DOE). The U.S. DOE’s National Energy Technology Laboratory’s (NETL) Geomaterials Characterization Laboratory has performed non-destructive core characterizations on energy-relevant rock cores for the past decade. During this time, NETL has published over 36 technical reports and made the associated data publicly available. Much of this work focuses on unconventional shale gas, subsurface carbon storage systems, and carbon-ore. These efforts provide cm-scale petrophysical and elemental data, photographic documentation, detailed core descriptions, and computed tomography (CT) data for each well. This provides a first phase prospecting resource for CM/REE resources and can provide a map for pin-pointing intervals and lithologies for further development. Using historical core characterization data from 12 Marcellus wells from the western portion of the Appalachian Basin, this study builds an improved understanding of the chemostratigraphy of the basin. X-ray fluorescence (XRF) and CT images were used to determine lithologic intervals and potential ore bodies for further analysis, including benchtop digestion and inductively coupled plasma mass spectrometry (ICP-MS) to better understand the CM/REE enrichments.
Despite the availability of computational resources and advancements in numerical computing capabilities, the multiscale models core to understanding, predicting the behaviors of, and designing energy and environmental systems involving porous media are still 1.) developed through by-hand derivations and 2.) limited by many methodological assumptions employed during model derivation. As a result, the advancement of effective media models for engineering DOE mission-critical systems (e.g., batteries, flow batteries, electrolyzers, geothermal systems, subsurface chemical storage systems, etc.) is slow (i.e., it takes years for models to traverse from stages of “development” to “practical utilization”), hindering our ability to effectively optimize such systems and stay at the cutting-edge of the energy frontier. In this work, we aimed to address these limitations by 1.) automating and accelerating multiscale model derivation via symbolic computing and 2.) develop a novel multiscale modeling methodology for flow and transport through porous media that avoids the typical assumptions hindering previous models. As a result of our efforts, we 1.) developed a hybrid symbolic-numeric code called Fouriera for fully-automating the implementation of multiphysical and phase-field models via the Fourier spectral method for materials science research, and 2.) advanced a multiscale modeling methodology called The Method of Finite Averages that rigorously predicts the behaviors of flow and transport through heterogeneous porous media under the influence of non-local effects and strong advection. Ultimately, these deliverables provide strong foundations from which further efforts can advance multiscale modeling tools and capabilities that do not intrinsically rely on 1.) the speed and mathematical capabilities of humans, nor 2.) the methodological assumptions limiting current models.
The habitable fraction of a planet's surface is important for the detectability of surface biosignatures. The extent and distribution of habitable areas are influenced by external parameters that control the planet's climate, atmospheric circulation, and hydrological cycle. We explore these issues using the ROCKE-3D general circulation model, focusing on terrestrial water fluxes and thus the potential for the existence of complex life on land. Habitability is examined as a function of insolation and planet rotation for an Earth-like world with zero obliquity and eccentricity orbiting the Sun. We assess fractional habitability using an aridity index that measures the net supply of water to the land. Earth-like planets become "superhabitable" (a larger habitable surface area than Earth) as insolation and day-length increase because their climates become more equable, reminiscent of past warm periods on Earth when complex life was abundant and widespread. The most slowly rotating, most highly irradiated planets, though, occupy a hydrological regime unlike any on Earth, with extremely warm, humid conditions at high latitudes but little rain and subsurface water storage. Clouds increasingly obscure the surface as insolation increases, but visibility improves for modest increases in rotation period. Thus, moderately slowly rotating rocky planets with insolation near or somewhat greater than modern Earth's appear to be promising targets for surface characterization by a future direct imaging mission.
The habitable fraction of a planet's surface is important for the detectability of surface biosignatures. The extent and distribution of habitable areas are influenced by external parameters that control the planet's climate, atmospheric circulation, and hydrological cycle. We explore these issues using the ROCKE-3D general circulation model, focusing on terrestrial water fluxes and thus the potential for the existence of complex life on land. Habitability is examined as a function of insolation and planet rotation for an Earth-like world with zero obliquity and eccentricity orbiting the Sun. We assess fractional habitability using an aridity index that measures the net supply of water to the land. Earth-like planets become "superhabitable" (a larger habitable surface area than Earth) as insolation and day-length increase because their climates become more equable, reminiscent of past warm periods on Earth when complex life was abundant and widespread. The most slowly rotating, most highly irradiated planets, though, occupy a hydrological regime unlike any on Earth, with extremely warm, humid conditions at high latitudes but little rain and subsurface water storage. Clouds increasingly obscure the surface as insolation increases, but visibility improves for modest increases in rotation period. Thus, moderately slowly rotating rocky planets with insolation near or somewhat greater than modern Earth's appear to be promising targets for surface characterization by a future direct imaging mission.
Funded in 2023 by the U.S. Department of Energy’s Phase II Carbon Storage Assurance Facility Enterprise (CarbonSAFE) initiative, a Heidelberg Materials cement plant in Mitchell, Indiana, is currently being evaluated as a potential Carbon Capture and Storage (CCS) subsurface injection site. The Heidelberg CCS project targets the middle to upper Prairie du Chien Group (Early Ordovician) in southwestern Indiana. Assessment of reservoir feasibility requires collection of field data, seismic surveys, well-log correlation, geologic modeling, characterization well drilling, well testing, and reservoir simulation. However, the proposed Heidelberg CCS site is in a data-limited region, lacking both outcrop analogs and deep wells penetrating the target interval, which makes geologic modelling difficult prior to drilling a characterization well. To directly address this problem, the present study was undertaken to understand the sedimentologic composition and stratigraphic architecture of the Prairie du Chien Group from analogous outcrops and cores in the Illinois Basin and adjacent regions.
The subsurface confinement of anthropogenic carbon dioxide (CO2) demands robust risk assessment methodologies to identify potential leakage pathways. Legacy wells within the Area of Review (AoR) represent one potential leakage pathway. Robust methodologies require enormous amounts of data, which are not available for many old legacy wells. This study strategically categorizes 4386 legacy wells within the AoR of a potential CO2 storage site in the Illinois basin and identifies the high-risk wells by leveraging publicly available data—reports and well logs submitted to state regulatory agencies. Wells were categorized based on their proximity to the injection well location, depth, the mechanical integrity of well barriers, and the accessibility to these wells throughout the project lifecycle. Wells posing immediate risks were identified, guiding prioritized corrective actions and monitoring plans. Out of 4386 wells, 54 have high priority for corrective action, 10 have medium priority, and the remainder are of low priority. Case study results from the Illinois basin demonstrate the effectiveness and applicability of this approach, to assess the risk associated with legacy wells within the AoR of potential CO2 storage site, strategically categorizing over 4000 such wells despite data limitations.
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.
Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.
Large-scale integration of energy storage on the electric grid will be essential to enabling greater penetration of intermittent renewable energy sources, modernizing the grid for increased flexibility security, reliability, and resilience, and enabling cleaner forms of transportation. The purpose of this report is to summarize Sandia's research and capabilities in energy storage and to provide a preliminary roadmap for future efforts in this area that can address the ongoing program needs of DOE and the nation. Mission and vision statements are first presented followed by an overview of the organizational structure at Sandia that provides support and activities in energy storage. Then, a summary of Sandia's energy storage capabilities is presented by technology, including battery storage and materials, power conversion and electronics, subsurface-based energy storage, thermal/thermochemical energy storage, hydrogen storage, data analytics/systems optimization/controls, safety of energy storage systems, and testing/demonstrations/model validation. A summary of identified gaps and needs is also presented for each technology and capability.
The volume of public geologic carbon storage (GCS) data resources has continued to increase in recent years as the result of an increase in funding from government, industry, and academia towards national, basin, regional and field scale studies to ensure carbon capture and storage becomes a commercially viable operation. Despite the increasing volume of data, GCS data applied towards analyses such as geologic, cost, and risk modeling continues to be multi-sourced and often disparate in nature, published across government agencies, websites, data repositories and buried in derivative reports and documents. Much of the time preparing for an analysis and derivative product development is spent collecting, aggregating, transforming and preparing input data. There have been significant efforts within the DOE National Energy Technology Laboratory’s Carbon Storage Program to optimize multi-source, multi-scale subsurface geologic data curation and aggregation to support data discovery, interoperability, and reuse. Methods include the use of artificial intelligence, machine learning, and data science techniques. This talk will discuss the workflows, best practices, and processes developed to support the aggregation and curation of data through the whole system – surface to subsurface data - that support multi-scale, multi-purpose analysis for carbon storage research.
Saline formations are attractive geologic reservoirs for permanent carbon dioxide (CO 2 ) storage. Here, the U.S. Department of Energy's National Energy Technology Laboratory (DOE-NETL) has worked to develop and refine methods and tools for the calculation of CO 2 storage potential in subsurface reservoirs. DOE-NETL's CO 2 -SCREEN provides an online tool for executing these storage methods. CO 2 storage efficiency terms are input parameters in DOE-NETL's methods and equations embedded in the CO 2 -SCREEN, which assesses pore space available for CO 2 storage. In this work, a modeling workflow was initiated to refine two CO 2 storage efficiency terms - volumetric displacement (E V ) and microscopic displacement (E d ). The models are based on new experimental relative permeability data that are specific to homogenous lithology and depositional environments of key subsurface saline formations targeted for CO 2 storage. In future work, heterogenous features will be added to this initial modeling effort to update efficiency factors as described in DOE-NETL's methods and CO 2 -SCREEN tool. E V accounts for the volume utilized in the reservoir under the areal plume, while E d accounts for saturation values in the plume to assess efficiency of CO 2 storage at the pore scale. The results of this work are significant in that prior values were based on a limited geologically non-specific relative permeability data set that were collected prior to 2009. Specifically, we applied numerical simulations using TOUGH3 models to update CO 2 storage efficiency values for supercritical CO 2 injection into brine-saturated reservoirs for three lithologies (clastics, limestone, dolomite) and six depositional environments (Marginal Marine, Strand Plain, Deltaic Complex Fluvial, Aeolian, Shallow Marine, and Reef) that have a high potential for geologic CO 2 storage. Experimental relative permeability data in cores from these environments were utilized in the models with corresponding rock type/sedimentary environment. Results of this study showed that dolomite followed by limestone generated higher ranges of storage efficiency compared to clastics. The updated values provided a tighter efficiency range for clastics, lower P 10 but higher P 90 range for limestone, and higher P 10 and P 90 for dolomite. In general, tighter reservoirs with relatively low permeability and porosity were associated with higher E V and E d , showing efficient reservoir and pore utilization in these scenarios. High reservoir pressure and temperature associated with increasing depth increased the E V , and high CO 2 injection rates resulted in increases in E V and E d , while the impact of permeability anisotropy was minimal after the 30-year injection period.