Multidimensional, experimental and modeling evaluation of permeability evolution, the Caney Shale Field Lab, OK, USA
Multidimensional, experimental and modeling evaluation of permeability evolution, the Caney Shale Field Lab, OK, USA
Katende, A.↗
SEARCH · Engineering Papers
Results for “subsurface permeability”
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Surface effects on deuterium permeation through vanadium membranes
Dense vanadium-based membranes offer high permeability and perfect selectivity to hydrogen isotopes, maintain favorable neutronic properties, and are compatible with liquid metals such as PbLi. These properties make vanadium membranes a promising fusion fuel cycle technology for processes such as tritium extraction from PbLi and exhaust processing. Surface contamination has a deleterious effect on the gas-phase hydrogen permeation through vanadium, and the reported permeabilities range from 10 -14 to 10 -7 mol m -1 s -1 Pa -0.5 . Thin dense films of palladium applied to clean vanadium surfaces enable a consistently high hydrogen permeability. In this study, uncoated vanadium resulted in deuterium permeabilities ranging from 2.8 × 10 -11 to 6.4 × 10 -9 mol m -1 s -1 Pa -0.5 at 300 °C–700 °C, respectively. Post-test analysis revealed a VO x surface layer and VC x subsurface layer formed on the feed side, while the as-received surface oxide dissolved leaving a submonolayer oxide on the permeate surface. Furthermore, the Pd-coated V resulted in a maximum deuterium permeability of 2.1 × 10 -7 m -1 s -1 Pa -0.5 at 375 °C upon activation of the Pd surface by oxidation and reduction. The deuterium permeation declined upon heating to 500 °C due to intermetallic diffusion between the Pd and V. The Mo 2 C-coated V resulted in deuterium permeabilities ranging from 2.7 × 10 -10 to 1.8 × 10-9 at 500 °C–700 °C, respectively, and a post-test analysis found the carbon in the Mo 2 C layer had dissolved into the V near the interface.
13 HYDRO ENERGY↗
Soft interface instability and gas flow channeling in low-permeability deformable media
Understanding gas percolation through a clay layer or a shale formation is of great importance for the development of a geologic repository for nuclear waste disposal, a subsurface system for gas storage, and an engineering approach for hydrocarbon extraction from unconventional reservoirs. Gas injection experiments have revealed complex dynamic behaviours of gas percolation through water saturated compacted bentonite, characterized by a high breakthrough pressure, rapid breakthrough, a pressure/stress decay after the breakthrough, a relatively high migration rate, high-frequency periodic/nonperiodic variations in flow rate, stepwise rate reductions during relaxation, and low gas saturation over the whole process, all indicating channelling nature of the processes. Using linear stability analyses, we show that this channelling can autonomously emerge from the instability of the deformable interface between the injected gas and the compacted bentonite matrix driven by local stress concentration, pore dilation, and hydrologic gradient. Channel patterns formed would possess a fractal geometry. We further show that, once a percolating channel is established, the gas injected would percolate through the channel in a chain of gas bubbles, also due to the interface instability, resulting in periodic/chaotic variations in gas flow rate. Our work provides a unified explanation for key features observed for gas percolation in low-permeability deformable media. The work also suggests a possibility of designing an engineered barrier system for a nuclear waste repository that can have controllable gas release while limit water transport.
Bentonite↗
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↗
Viscous Behavior of Clay‐Rich Rocks and Its Role in Focused Fluid Flow
Abstract Focused fluid flow is common in sedimentary basins worldwide, where flow structures often penetrate through sandy reservoir rocks, and clay‐rich caprocks. To better understand the mechanisms forming such structures, the impacts of the viscoelastic deformation and strongly nonlinear porosity‐dependent permeability of clay‐rich materials are assessed from an experimental and numerical modeling perspective. The experimental methods to measure the poroviscoelastic and transport properties of intact and remolded shale have been developed, and the experimental data is used to constrain the numerical simulations. It is demonstrated that viscoelastic deformation combined with nonlinear porosity‐dependent permeability triggers the development of localized flow channels, often imaged as seismic chimneys. The permeability inside a channel increases by several orders of magnitude compared to the background values. In addition, the propagation time scale and the channel size strongly depend on the material properties of the fluid and the rock. The time‐dependent behavior of the clay‐rich rock may play a key role in the long‐term integrity of the subsurface formations.
58 GEOSCIENCES↗
GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources
The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.
15 GEOTHERMAL ENERGY↗
Zero-Valent Iron Permeable Reactive Barrier to Remediate Volatile Organic Compounds in Groundwater - 20209
The US DOE-SRS, the US EPA, and the South Carolina Department of Health and Environmental Control determined it was appropriate to perform a non-time critical removal action at the P-Area Groundwater Operable Unit at the SRS to reduce the mass and downgradient transport of trichloroethylene in the P-Area groundwater plume. Contaminated groundwater discharges to a nearby stream, Steel Creek, within the SRS boundaries, resulting in trichloroethylene concentrations above the maximum contaminant level. Impact to surface water is limited in areal extent and supported by recently collected characterization data. The P-Area Groundwater Operable Unit encompasses the groundwater beneath an industrial area within SRS, P Area, where the P-Reactor once operated. The boundaries of the P-Area Groundwater Operable Unit extend northwest to Steel Creek, northeast toward PAR Pond, and southeast to Meyers Branch. Groundwater in the Upper Three Runs Aquifer of the P-Area Groundwater Operable Unit has been impacted by reactor and facility operations between 1954 and 1991, including tritium and volatile organic compounds. The P-Area surface units contributing to groundwater contamination were remediated as part of the P-Area Operable Unit in 2011. The P-Reactor closure is one of the first of its kind in the DOE Complex and is one of only a few full-sized production reactors in the US to undergo completion of final closure activities. The nature and extent of groundwater contamination was determined using a variety of investigative approaches such as groundwater monitoring wells, direct-push technology, and surface water samples. Groundwater contamination associated with trichloroethylene is primarily exhibited in a narrow plume that extends from the source area at P-Reactor and west to Steel Creek. Maximum contaminant level exceedances in groundwater occur over an area of ∼6.9 hectares for trichloroethylene with concentrations as high as 7.7 milligrams per liter. To the west of the P-Area facility area, the trichloroethylene groundwater plume is controlled by a buried geologic feature, assumed to be an old stream bed, that further narrows the groundwater plume in what has been designated as the 'neck area.' This narrowing of the groundwater plume provides an ideal location for a treatment barrier. The non-time critical removal action alternative chosen is to install a zero-valent iron permeable reactive barrier within the neck area of the trichloroethylene groundwater plume, perpendicular to groundwater flow direction. This technology will provide a treatment barrier that will reduce trichloroethylene groundwater concentrations by 90% and has an anticipated useful life of at least 25 years. A pre-design investigation was performed in the neck area to confirm site lithology, hydrogeology, geochemistry, and extent of trichloroethylene contamination prior to a final design. A treatability study, conducted as part of the pre-design investigation, indicated that the subsurface and groundwater in the PArea Groundwater Operable Unit is compatible with the zero-valent iron and will not lead to excessive buildup from mineralization/precipitation or biofouling. Probabilistic modeling was conducted using field and laboratory data to determine the expected performance of the zero-valent iron permeable reactive barrier. The model simulations indicated that a 3.81-centimeter thick barrier would provide greater than 90% reduction of trichloroethylene groundwater concentration. The final design of the zero-valent iron permeable reactive barrier is a barrier that will extend 80.5 linear meters in a 'zigzag' orientation to best transect the trichloroethylene plume and account for varying groundwater flow. The barrier will be installed from 13.7 meters below ground surface to 41.1 meters below ground surface for 65.8 linear meters and from 13.7 meters below ground surface to 36.6 meters below ground surface for 14.6 linear meters, the base of which is 'keyed' into a low permeability zone. The barrier is designed to a thickness of 10.2 centimeters, which was determined to reduce the trichloroethylene groundwater concentrations by greater than 90% with a safety factor of 2.67. A total of approximately 689 metric tons of zero-valent iron will be injected through 22 injection wells spaced 3.66 meters apart, using guar to suspend the zero-valent iron. Zero-valent iron permeable reactive barrier construction will be monitored through 23 installed resistivity receivers offset 7.32 meters from the zerovalent iron permeable reactive barrier. The zero-valent iron will be energized with a low-voltage 100 Hertz signal during injection and will be monitored using the resistivity receivers to ensure complete coalescence of the zero-valent iron permeable reactive barrier. The zero-valent iron was sized to have a hydraulic conductivity greater than the natural subsurface, thus promoting groundwater flow through the barrier. As contaminated groundwater contacts the zero-valent iron, volatile organic compounds, including trichloroethylene, are immediately degraded to harmless compounds such as ethylene. The performance of the zero-valent iron permeable reactive barrier will be monitored using three upgradient monitoring well clusters, six downgradient monitoring well clusters, and four in-wall monitoring wells. The in-wall monitoring wells will indicate immediate reduction of trichloroethylene mass in the groundwater and allow analyses of the zero-valent iron permeable reactive barrier health. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint
Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.
40 EE - Geothermal Technologies Office (EE-4G)↗
Flow Channeling in Fracture Networks: Characterizing the Effect of Density on Preferential Flow Path Formation
Flow channelization is a commonly observed phenomenon in fractured subsurface media where the flow of fluids is restricted primarily to highly transmissive fracture networks surrounded by a low-permeability rock matrix. The multiscale structural heterogeneity of these networks results in multiscale flow channelization where preferential flow paths form at length scales ranging from the entire system down to the subfracture size. We present an analysis of how one of the largest scales in fractured media, the network density, influences the degree of flow channeling that occurs using an ensemble of semigeneric three-dimensional discrete fracture network (DFN) simulations. We construct 10 DFNs, whose fracture lengths follow a power law distribution, at four densities for a total of 40 networks. We characterize their structure in terms of the network topology and geometry. Eulerian and Lagrangian observations of the steady-state flow fields obtained within the networks are used to quantify the degree of flow channelization at the network scale. We introduce a measure for the importance-ranking/hierarchy of different flow paths in the network using graph-based analysis of Lagrangian transport by which the degree of flow channeling between networks is compared. These flow observations are then linked to the structural properties of the networks. In general, network-scale flow channeling decreases as the network density increases. However, at low densities, there is more uniform flow within the entire connected network than in high-density networks. We also demonstrate how standard transport observables can be used to infer the degree of flow channelization occurring within a fracture network.
58 GEOSCIENCES↗
Numerical investigations to identify environmental factors for field-scale reactive transport of pathogens at riverbank filtration sites
While induced bank filtration is a proven method for facilitating sustainable drinking water production, it is at risk from surface water contaminations (e.g., pathogens). Induced bank filtration and pathogen transport in groundwater have been studied extensively. However, long-term studies that consider real-world conditions are missing. These conditions include seasonal changes to environmental conditions and waterworks operations. Therefore, to analyze the effect of seasonal changes on the transport of human pathogenic viruses and their indicators in induced bank filtration, concentrations of adenoviruses and pathogen indicators were monitored over 16 months at an active bank filtration plant at the Rhine River, in Düsseldorf (Germany). Based on this data, a 2D groundwater model was created in PFLOTRAN that simulated flow, heat transport, conservative transport of chloride and the resulting electrical conductivity, reactive transport of oxygen and nitrate, and colloid-based transport of coliforms, somatic coliphages, and adenoviruses. The results show that reduced travel time was the key factor determining periods with a low removal of coliforms and somatic coliphages in the aquifer. Travel time was controlled by river level variations during rainy seasons, and the waterworks extraction rates during dry seasons. Further, for adenovirus transport, travel distance in the subsurface appeared to be the key factor, while travel time had no significant impact. Coliform removal increased when the colmation layer permeability decreased, while coliphage and adenovirus removal was unaffected by the colmation layer permeability. Seasonal changes in temperature and oxygen content did not significantly impact the removal of coliphages and adenoviruses in groundwater. Denitrifying conditions correlated with a lowered coliform removal, but the modelling could not establish a connection between denitrifying conditions and coliform removal. Our study showed that removal of pathogens and pathogen indicators at induced bank filtration plants varies greatly in time and space (e.g., for coliforms from 1 to 4 log-levels at 20m travel distance), and that adenovirus transport differs considerably from transport of coliforms and somatic coliphages.
59 BASIC BIOLOGICAL SCIENCES↗
Insights from a coupled thermo-hydro-mechanical analysis of a layered high-temperature thermal energy storage reservoir
Coupled thermal-hydraulic-mechanical (THM) modeling is applied to investigate the performance of a seasonal high-temperature aquifer thermal energy storage operation based on data and conditions from current site investigations at the Geostorage Forsthaus pilot project in Bern (Switzerland). The model includes subhorizontal sand lenses of various lengths and dips that are embedded in a low permeability clay matrix. Thermal energy storage is simulated by seasonal injection and withdrawal of hot (up to 90 °C) water from a main well, with reservoir pressure regulated by two auxiliary wells at a distance of about 70 m from the main well. The results show how targeted injection into deeper permeable storage formations, along with active deep well pressure control, can effectively minimize geomechanical impact and the potential risk of damaging subsurface storage and sealing formations, or even surface facilities. With such pressure control, the subsurface mechanical responses are dominated by thermal strain and stress, which can be monitored with subsurface fiber optics. The study demonstrates how coupled THM modeling can be applied for the design of a safe and efficient thermal energy storage operation, and how subsurface fiber optic monitoring can be applied for performance confirmation, allowing for more confident operational forecasting.
Rutqvist, Jonny↗
On the viability of stimulated hydrogen generation from iron-rich formations
Hydrogen-based technologies present a promising solution for the global energy transition. In addition to electrolytic production, subsurface geological formations provide a potential natural source of hydrogen. Iron-rich ultramafic rocks, in particular, are favorable for hydrogen generation through natural processes such as serpentinization. Naturally occurring reactions and migration can be enhanced through various types of stimulation, including thermal, hydraulic, and chemical treatment. Through numerical simulations, we analyzed the complex interplay of factors influencing the production and migration within the subsurface, emphasizing the importance of different stimulation techniques, catalysts, and conditions. Our findings indicate that key parameters, such as damage zone permeability and width, significantly impact producible hydrogen mass. Our results indicate that a combination of large damage zone widths, high permeability, and a stimulated reaction rate of 1 × 10 -9 can yield economically viable production rates of up to 1 kg s -1 at the wellhead. Moreover, the availability of ferrous iron, rather than the serpentinization rate itself, has been identified as the primary limiting factor in achieving economically sustainable hydrogen production. In conclusion, while an unstimulated rock volume of 0.165 km 3 yields only 45t of hydrogen in two years, various stimulation techniques can increase production to 18500t.
08 - HYDROGEN↗
Temperature and Pressure Dependence of Salt–Brine Dihedral Angles in the Subsurface
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration
Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.
42 ENGINEERING↗
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↗
Hydrologic Modeling of Subsurface Brine Density Stratification for Deep Borehole Disposal - 20213
Density-dependent and topographically-driven subsurface brine flow was modeled in regional-scale and Deep Borehole Disposal-scale domains under varying conditions. These conditions included variable basement permeability, brine density, and hydraulic head. Findings revealed the possibility of hydraulic isolation of deep basement for at least 1 million years under appropriate conditions, which supports the viability of the Deep Borehole Disposal concept. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow 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 relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. 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. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.
15 GEOTHERMAL ENERGY↗