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Chemical impacts of subsurface CO 2 and brine on shallow groundwater quality
Leakage from geologic CO 2 sequestration (GCS) sites to overlying shallow drinking water aquifers is a tangible risk. A primary purpose of this study is to assess the potential impacts of CO 2 leakage into a fresh-water aquifer with associated CO 2 -water-sediment interactions. Here, the study site is the Ogallala aquifer overlying an active demonstration-scale GCS site in north Texas, USA. Using the results of combined batch experiments and reactive transport simulations, we discuss the effects of salinity on potential trace metal release and the potential for groundwater quality recovery after leakage ceases.
Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys
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North Atlantic subsurface temperature response controlled by effective freshwater input in “Heinrich” events
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Unsupervised learning monitors the carbon-dioxide plume in the subsurface carbon storage reservoir
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Coreflood on a chip: Core-scale micromodels for subsurface applications
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Densely populated biofilms and linked iron and sulfur cycles in the fractured-rock continental subsurface
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Play Fairway analysis of geothermal resources across the State of Hawai‘i: 4. Updates with new groundwater chemistry, subsurface stress analysis, and focused geophysical surveys
This paper is the fourth in a series on a play fairway analysis of geothermal resources across the State of Hawai‘i. Here we describe recent exploration activities that include groundwater sampling in ten locations statewide, as well as geophysical surveys on Lana‘i, across the SW rift of Haleakala Volcano (Maui), and surrounding Mauna Kea Volcano (Hawai‘i Island). We derive a first-order method for computing topographic stress using Green’s functions, finding that topography can induce appreciable crustal stress. We develop a new method for incorporating depth information about resistivity, density, and topographic stresses into our previously published equations to calculate resource probability and confidence. We incorporate newly collected and modeled data into our calculations to update statewide maps of probability and confidence. Lana‘i Island and southeast Mauna Kea are identified as the top targets for exploratory drilling. The east rift of Haleakala Volcano, the southern-most region of Mauna Loa’s SW rift (Hawai‘i Island), and central Kauai are identified as targets for geophysical surveying.
Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage
In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.
In situ precipitation of hydrous ferric oxide (HFO) for remediation of subsurface iodine contamination
A practical approach for in-situ hydrous ferric oxide (HFO) precipitation was developed for iodine immobilization under field-scale conditions at the Hanford Site. A series of 1D meter-long bench top column experiments packed with Hanford sediments was conducted with a single acidic ferric solution (0.1 M, pH = 1.5) injection. Because carbonate and clay minerals are widely present in sediments, self-pH buffering of the injected acidic ferric solution occurred due to mineral dissolution, leading to HFO precipitation under a neutral condition. Up to ~12 mg/g HFO successfully precipitated and evenly distributed in the column sediments, and the remobilization of the neo-formed HFO precipitates was limited (< 4 wt.% after 100 pore volumes of flushing). The transport of IO3- in the HFO-amended sediments was strongly retarded through both adsorption and co-precipitation processes. However, reversible adsorption of iodine on HFO was observed, which might limit its application to slow moving groundwater systems.
Numerical evaluation of photosensitive tracers as a strategy for separating surface and subsurface transient storage in streams
For this work, we numerically evaluated photosensitive tracers as a potential strategy for separating the effects of surface and hyporheic storage zones (SSZs and HSZs, respectively) on stream corridor transport. Correctly separating HSZ and SSZ effects is critical to estimating the hydro-biogeochemical function of a stream because HSZs and SSZs expose solutes to significantly different biogeochemical conditions, like sunlight exposure, microbial processes, and oxygen availability. Our numerical experiments used a multiscale river-corridor transport model implemented in the ATS code, which accommodates multiple storage zones with distinct travel time distributions and biogeochemical reactions. For parameter inferences, we used Bayesian inverse modeling. We found that breakthrough curves for photo-decaying tracers from day and night injection can delineate surface and hyporheic transient storage contributions, but only when interpreted jointly through a two-storage zone model. Numerical experiments that used only daytime injection or interpreted breakthrough curves with a single storage zone model yielded good fit to breakthrough curves, but parameter estimates were biased and controlling processes misattributed, examples of good model fits for the wrong reasons. Using those biased parameter estimates in reactive transport simulations resulted in significantly different projections of denitrification, which underscores the potential for stream function to be mischaracterized if tracer tests are interpreted through an inappropriately simplified model for transient storage. More generally, this study highlights the role of modeling in evaluating the experimental design and identifying the potential of system mischaracterization and its implications.
Heat pulse testing at monitoring wells to estimate subsurface fluid velocities in geological CO 2 storage
Monitoring the injected CO 2 during geological CO 2 storage (GCS) is essential to assure containment and identify CO 2 leakage. Here in this work, a new approach is introduced to estimate the evolution of the downhole fluid velocity at a monitoring well and identify CO 2 arrival time using in-well heat pulse/tracer test. The proposed technique involves using a downhole heater to generate a series of heat pulses and measuring their corresponding temperature response. The surface temperature of the downhole heater is controlled by the supplied electrical power and the heat loss by convection to the surroundings. Convective heat transfer is well described using Newton's law of cooling in which the temperature difference between the heater and the surrounding fluids drives the heat transfer, for which the convection heat transfer coefficient (h) controls the magnitude of heat loss. Among various factors that control h, it depends on the type of the flowing fluid and its velocity. Through analyzing the measured temperature at different heat pulses, the changes in h - due to mobilization of the in-situ brine or CO 2 arrival - can be estimated. Consequently, the velocity of the flowing fluid across the heater can be obtained. Since heat transfer by convection is sensitive to the type of the surrounding fluid, intrusion of CO 2 can be detected from the relatively higher surface temperature obtained at CO 2 arrival. Churchill and Bernstein (1977)'s correlation is adopted to estimate the change of fluid velocity in terms of the change in h. To demonstrate the validity of the proposed technique, the results are applied and validated against those of COMSOL Multiphysics simulation tool for single-phase brine (before CO 2 arrival) and single-phase CO 2 (after CO 2 arrival). The observed temperature heating is sensitive to the flowing fluid velocity and fluid type. The temperature signal observed at CO 2 arrival is large and easily detectable using temperature monitoring tool which provides reliable indication for tracking CO 2 arrival at monitoring wells compared with passive temperature monitoring. The results obtained using the proposed technique agree very well with the numerical results obtained from the simulation tool with a maximum estimation error of 7 percent.
Reproducible responses of geochemical and microbial successional patterns in the subsurface to carbon source amendment
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