Fluid saturation estimation using Full Waveform Inversion (FWI): a controlled laboratory experiment

Includes bibliographical references.2024 Spring.This thesis presents a comprehensive study on the application of Full Waveform Inversion (FWI) and fluid substitution analysis for estimating fluid saturation in a controlled laboratory setting. Monitoring fluid injection, essential for processes such as carbon capture and storage (CCS), enhanced oil recovery, and hydraulic fracturing, is crucial in minimizing environmental and operational risks. Through a novel approach, this study applies time-lapse FWI (4D FWI) in a lab experiment to detect changes in fluid saturation within a rock sample, aiming to evaluate the effectiveness of traditional methods like Gassmann fluid substitution in capturing the complexities of fluid-rock interactions under partially saturated conditions. The methodology involves a detailed experimental setup for acoustic data acquisition on a Berea Sandstone sample, utilizing the 3D Acoustic Acquisition & Imaging System (WILD) for both baseline and monitor surveys. The study carefully outlines the process of mesh creation, data preprocessing, and the application of FWI to derive high-resolution P-wave velocity models. These models are then used to assess the validity of Gassmann fluid substitution in predicting fluid saturation changes, revealing discrepancies that suggest the need for enhanced models or additional considerations in fluid substitution analysis. Significant findings from the research include the observation of unexpected P-wave velocity reductions in partially saturated rocks after brine injection, challenging the conventional expectations based on Gassmann’s theory. This anomaly is attributed to attenuation and dispersion due to various factors, including patchy saturation, wave-induced fluid flow (WIFF), and changes in surface energy, underscoring the complex nature of fluid-rock interactions. The study also emphasizes the importance of careful sample selection, mesh optimization, and preprocessing techniques in improving the accuracy and reliability of FWI outcomes. The thesis concludes with valuable insights and recommendations for future research, emphasizing the need for continuous refinement of FWI parameters, creating more accurate fluid substitution models, and incorporating advanced computational and data collection methods. This research marks a significant advancement in the application of 4D FWI in laboratory experiments, offering a new perspective on fluid injection monitoring and the potential for improved reservoir characterization and management

Sphalerite

Photographed by Ron Wolf.Dull grey sphalerite with metallic highlights on fine grey crystal matrix

Colemanite

Photographed by Ron Wolf.Translucent vitreous white colemanite, Death Valley, Inyo County, California

Leadhillite

Photographed by Ron Wolf.Glassy round cluster of pale blue leadhillite on white and brown matrix

Cause of geothermal temperature anomalies in Wattenberg field: unraveling the impact of hydrothermal fluid movement in the Lyons formation

Includes bibliographical references.2024 Spring.The Wattenberg Field temperature anomaly solidified the field as the largest hydrocarbon producer in the Denver Basin and has spurred industry interest in using the field for geothermal energy and CCUS. While the presence and impact of the hot subsurface temperature anomaly is known, the cause of the anomaly is not. This study proposes that heat from northeastern Colorado Mineral Belt intrusives was conducted to reservoir rocks in the Greater Wattenberg Area via the subsurface circulation and migration of hydrothermal fluids along field-wide fault zones. Using core description, XRF, thin section microscopy, and FESEM analysis, pervasive hydrothermal fluid-rock interaction was observed in the Permian Lyons Formation. The Lyons Formation is the host of various ore minerals that are found to have been emplaced and nucleated in-situ: 1) low temperature (125℃-175℃) sulfides pyrite, marcasite, sphalerite and galena; 2) intermediate temperature (125℃-225℃) sulfarsenides of the Co-Ni-As-S system, including gersdorffite, nickeline, and clinosafflorite; and, 3) the high temperature (>225℃) sulfide chalcopyrite. Ore mineral emplacement is accompanied by various intermediate temperature (>150℃-200℃) gangue mineral alteration types such as albitization, dedolomitization, and generation of Fe-Mg-metal-rich clays. Spatial relationships between ore minerals of differing temperature regimes indicate that at least three episode of hydrothermal deposition occurred. By comparing the timing of northeastern CMB intrusions with the timing of dissolution-reprecipitation events in the Lyons, hydrothermal interaction is interpreted to have occurred in Wattenberg Field from the late Paleocene to late Miocene. Long-term heat conduction between hydrothermal fluids and the Greater Wattenberg Area sedimentary succession was facilitated by the high thermal conductivity of the Lyons Formation (3.56 Wm-1K-1) and maintained by the thick, insulating Mesozoic sedimentary succession – ultimately causing the Wattenberg Field temperature anomaly. Hydrothermal fluid-rock alteration begets critical implications for CCUS and geothermal activity in Wattenberg Field. Operators should investigate potentially detrimental chemical reactions that could occur between sequestered gases, sulfides, and sulfarsenides. Geothermal system designs should account for potential dissolution and remobilization of cementing metals that could alter reservoir characteristics and release metals to the groundwater supply. Overall, understanding the distribution and impact of hydrothermal alteration will improve energy exploration strategies in the Greater Wattenberg Area

Solvometallurgy for the dissolution of critical metals

Includes bibliographical references.2024 Spring.As natural deposits of metals used in energy technologies become scarce, there is a call to research new sources of these materials. Recycling spent energy technologies, such as batteries, could be the key to this newfound problem. A number of recycling methods have been developed; however, they require the use of harsh chemicals, produce dangerous side products, are not selective, or are expensive. There are three main categories of metal recycling methods: pyrometallurgy, biometallurgy, and solvometallurgy. Solvometallurgy provides the most promising route for developing an efficient, cost effective, selective, and safe metal recycling method. Recent studies have focused on the recycling of gold from electronic waste. Applying the findings from gold recycling systems, a new solvometallurgy system was created to achieve dissolution of Co. The use of N,N-dimethylformamide as an organic solvent, thiourea as a ligand, and 30% hydrogen peroxide as an oxidant was able to reach Co dissolution percentages of 70-77% in 60-80℃. This is an improvement upon methods currently being used at an industrial scale that require high temperatures and produce low efficiencies. The mechanism of organic aqua regia was also investigated to give a better understanding of a well-known solvometallurgy technique for future use in metal recycling

Energy models: dispatch and market impacts

Includes bibliographical references.2024 Spring.Optimization plays a crucial role across various facets of renewable and clean energy dispatch, mitigates the impacts of individual component failures, and incorporates additional services into dispatch models. Ultimately, these capabilities can address the impacts of design and dispatch models in varying socioeconomic settings. Many energy optimization models incorporate aspects of energy planning such as equipment inventory value, lead time, service costs, and health. Our study involves the lifespan analysis of heat exchangers within a concentrated solar power (CSP) plant. We model their thermal profiles, thermal-mechanical stresses, and fatigue using a reduced-order model and a fatigue damage tool. Our findings reveal that the evaporator's lifespan is 17.5 years, and the superheater's lifespan is 11 years, in contrast to an assumed design expectancy of 30 years. The primary cause of the failures is thermal stress in the tubesheets, which is consistent with industry reports. We utilize these updated lifespans to evaluate the gross revenue impact via simulation and optimization, and provide more realistic estimates of lead times than previously invoked. We observe a 3-4% decrease in gross revenue compared to original lifespan estimations. Hidden costs include equipment replacement and the possibility of losing a prior purchase agreement. Subsequently, our focus shifts to extending a CSP plant dispatch model to illustrate the benefits of incorporating an ancillary service, such as spinning reserves, which aid in maintaining grid reliability by requiring a 10-minute response time for a duration of two hours. The addition of this capability to a CSP plant's operation results in an annual profit increase of 3-7% across three distinct markets compared to its operation in the absence of this type of market. Lastly, we expand our investigation to assess the health impacts on socially vulnerable communities when the central grid fails. During power outages when microgrids are invoked to maintain power supply, a trade-off emerges between costs and energy demand satisfaction that disproportionately affects vulnerable communities reliant on medical devices. In addition, we evaluate the trade-offs between emissions reductions and costs, noting that more vulnerable communities experience higher mortality rates from particulate matter. When 80% of demand is met during an outage, only approximately 2% of the population is impacted by not having access to power for medical devices; but, meeting demand incurs a significant cost (i.e., a 25% increase in cost for an 80% satisfaction of demand during an outage). Conversely, the cost increases by approximately 1% with a reduction in emissions of 1% and 5%, resulting in a 4-9% reduction in mortality rates

Schorl on albite

Photographed by Ron Wolf.Dark glassy striated schorl crystals on tabular white plates of albite

Pyromorphite

Photographed by Ron Wolf.Vitreous prisms of yellow-orange pyromorphite

Scalable pipeline for antenna performance prediction based on data-informed machine learnng methods, A

Includes bibliographical references.2024 Summer.With the rapid deployment of fifth-generation communication (5G) and the exploration of sixth-generation (6G) communication, antenna engineers face increasing challenges in antenna design and integration. Before the fabrication, antenna engineers must establish specific objective functions with customized constraints based on the design requirements. Then, full-wave simulation tools and search algorithms are involved in optimizing the antenna. During the optimization, the antenna configurations are regarded as the inputs and the performance as the outputs. The trial-and-error iterations are utilized to minimize the cost between the current output and the design requirements. However, the full-wave simulation in each iteration is computationally intensive, and the search algorithms need to optimize their parameters according to the input properties and the customized value ranges; both processes are time-consuming. When there are multiple input variables, antenna engineers always trade off the inputs to get a convergent solution. The research presented here, based on the machine-learning (ML) point of view, provides a scalable pipeline with different data-informed machine-learning (DIML) workflows. The scalable pipeline here means an efficient objective to accelerate antenna design, in which a series of automated processes are established to reduce the optimization iterations. The pipeline explores the uncovered I/O relationship by the ML models as the probability estimators. The heuristic properties of the well-trained ML models provide reasonable performance predictions based on the random combination of inputs in near real-time matters, even if those combinations are not covered by the full-wave simulations. With the parallel-computing power of the graphic processing unit (GPU), the pipeline takes advantage of hardware acceleration from the beginning of electromagnetic (EM) full-wave simulation, data collection, and post-processing to the final ML validation. By applying the well-trained DIML models, the automatic pipeline provides reasonable prediction as the reference for antenna engineers during the design and integration process. This could reduce the full-wave simulation trials and minimize the iterations before finalizing the antenna configuration for fabrication. By discussing one of the most important factors of antenna performance, reflection coefficients S11, the pipeline achieves performance prediction of the patch antennas in a wide frequency range. The pipeline is proposed to show its generalization property, which makes it easy to implement on other designs. The fully automated simulation with data collection and the customized ML architecture provide the pipeline powerful scalability in further work with more antenna types and materials, more performance requirements, and wrapping as a pre-trained ML model for other antenna designs