Geosystems risk and uncertainty: The application of ChatGPT with targeted prompting
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The Geothermal Entrepreneurship Organization launched in 2019 with the goal of building a geothermal innovation ecosystem at the University of Texas at Austin (UT Austin), and in the State of Texas at large. The theses underlying the work of GEO were 1) that with targeted advocacy, recruitment, organization, and coalition building, research institutions with legacy excellence in petroleum and geosystems engineering could become engines of geothermal innovation, research and development; 2) that startups were the appropriate vehicle to speed these innovations from the lab into the field, and building a geothermal startup ecosystem in Texas would not only advance next generation geothermal concepts into the field, but also help spur oil and gas engagement in the space, and 3) that with targeted engagement, education, and recruitment across stakeholders in the oil and gas industry and the State of Texas generally, the oil and gas industry, and other legacy oil and gas entities in the State could become sources of large scale deployment of geothermal energy. The overall goal was to create a ‘snowball’ effect, where targeted impactful actions would catalyze self-sustaining, organic growth of a new geothermal ecosystem in the State of Texas. That goal was achieved through GEO’s work. To test its theses, GEO began work by interviewing and recruiting UT Austin faculty and alumni into geothermal. At the beginning of the GEO project, there was no geothermal activity ongoing within the UT Austin Petroleum and Geosystems Engineering Department, the Bureau of Economic Geology, or the Jackson School of Geoscience, and many faculty approached had not before considered how their skillsets might apply in the space. By the end of the project period, three major research consortia focused on geothermal were launched as a result of GEO’s work, one at the Bureau of Economic Geology, one within the Petroleum and Geosystems Engineering Department, and another organized by GEO across six research institutions across the State of Texas, called the Texas Geothermal Institute. Geothermal curricula was launched at UT Austin, and UT Austin began attracting new geothermal enthusiasts into its faculty, including Dr. Silviu Livescu, former Chief Scientist of Baker Hughes. Startups recruited and mentored by GEO launched, raised funding, and deployed (or are currently deploying) their concepts in the field. By the end of the project period, the GEO concept expanded to faculty beyond UT Austin to other institutions, like Texas A&M, the University of Houston, and Rice University, and geothermal engagement began at those institutions as well. Multiple faculty members and alumni across these institutions launched geothermal startup companies, launched geothermal research consortia, and/or began teaching geothermal courses. In 2020, GEO launched what was to become the largest geothermal conference in the world by its second year, ‘PIVOT – From Hydrocarbons to Heat’, and the resulting momentum catalyzed the Society of Petroleum Engineers to launch a geothermal technical section, drove more startups to launch out of the ecosystem, and drove actors in the State of Texas, NGOs, and stakeholders globally to become engaged. Riding this momentum, the Texas Geothermal Energy Alliance was launched, the first ever industry association dedicated to advancing geothermal energy in the State of Texas. The Texas geothermal ecosystem after only two years of building and support is now robust, quickly growing, and self-sustaining. By 2021, the Texas geothermal ecosystem had attracted the attention of philanthropists, funding entities, media, and influencers outside of Texas, and GEO’s executive director was invited to give a TED talk about oil and gas engagement in building the future of geothermal energy, which elevated the success of the ecosystem to a global audience.
Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.
Carbon mineralization in humidified carbon dioxide offers a promising route to mitigate anthropogenic emissions in a world stressed by water security. Despite its technological importance, our understanding of carbonation in water-poor environments lags, as traditional dissolution-precipitation pathways struggle to explain the adsorbed water nanofilm-mediated reactivity. Here, we utilize in operando X-ray diffraction (XRD) and advanced molecular simulations to investigate nanoconfined reactions driving forsterite carbonation, the magnesium-rich olivine. By examining magnesium ion dissolution and transport in atomistic simulations of the forsterite-water-carbon dioxide interface and comparing these with the in operando XRD activation energies, we identify both processes as rate-limiting at saturation. Our simulations reveal a mechanistic view of interfacial carbonation, where dissolution and precipitation are mediated by anomalous quasi two-dimensional diffusion. The transport process involves intermittent diffusive hopping in the desorbed state, separated by crawling events that are spatially short but temporally long. This understanding transcends carbon mineralization, with implications for understanding the transport of contaminants in geosystems, the design of multifunctional materials, water desalination, and molecular recognition systems.
The main objective of this project is to obtain a better understanding of the transport behavior and interactions of geocolloids in the presence of energy- related contaminants under bulk and nanoconfined conditions. In that, we first established solid protocols to synthesize and fabricate numerous types of geocolloids with a. various sizes ranging from 40 – 800 nm and b. different hydrophile-lipophile balance (HLB) ratios in the range of 25:75 to 75:25. Direct force measurements with geocolloids having different degrees of surface coverages (i.e. HLB to realistically mimic adsorption of energy related contaminants) were conducted using the Surface Forces Apparatus (SFA) over a distance regime starting from 8μm all the way down to molecular contact. Repulsive forces were observed on approach starting from > 3μm, followed by an exponential increase of which magnitude appears to be larger than a decay length obtained from Derjaguin–Landau–Verwey–Overbeek (DLVO) theory in pure water. When the geocolloids were confined in salted water, the magnitude of onset of repulsion was varied as a function of salinity in solution, which can significantly alter a purely repulsive screened electrostatic (coulombic) interaction arising from, among geocolloids as well as between geocolloids and geosurfaces. The viscosity and flow characteristics of geocolloidal suspensions at different degrees of confinement were also investigated where we identified highly discontinuous rheological behaviors below a critical nanoconfinement level. We anticipate that the knowledge gained through this study will enable the scientists and researchers to better assess transport and fate behaviors of geocolloidal dispersions that can carry energy-related contaminants under realistically emulated geosystem.
The Lewis and Clark Geosystem is an online collection of private, state, local, and Federal data resources associated with the geography of the Lewis and Clark Expedition. Data were compiled from key partners including NASA s Stennis Space Center, the U.S. Army Corps of Engineers, the U.S. Fish and Wildlife Service, the U.S. Geological Survey (USGS), the University of Montana, the U.S. Department of Agriculture Forest Service, and from a collection of Lewis and Clark scholars. It combines modern views of the landscape with historical aerial photography, cartography, and other geographical data resources and historical sources, including: The Journals of the Lewis and Clark Expedition, the Academy of Natural Science's Lewis and Clark Herbarium, high-resolution copies of the American Philosophical Society s primary-source Lewis and Clark Journals, The Library of Congress Lewis and Clark cartography collection, as well as artifacts from the Smithsonian Institution and other sources.
When performing Inertial Navigation System (INS) testing at the Marshall Space Flight Center's (MSFC) Contact Dynamics Simulation Laboratory (CDSL) early in 2017, a Leica Geosystems AT901 Laser Tracker system (LLT) measured the twist & sway trajectories as generated by the 6 Degree Of Freedom (6DOF) Table in the CDSL. These LLT measured trajectories were used in the INS software model validation effort. Several challenges were identified and overcome during the preparation for the INS testing, as well as numerous lessons learned. These challenges included determining the position and attitude of the LLT with respect to an INS-shared coordinate frame using surveyed monument locations in the CDSL and the accompanying mathematical transformation, accurately measuring the spatial relationship between the INS and a 6DOF tracking probe due to lack of INS visibility from the LLT location, obtaining the data from the LLT during a test, determining how to process the results for comparison with INS data in time and frequency domains, and using a sensitivity analysis of the results to verify the quality of the results. While many of these challenges were identified and overcome before or during testing, a significant lesson on test set-up was not learned until later in the data analysis process. It was found that a combination of trajectory-dependent gimbal locking and environmental noise introduced non-negligible noise in the angular measurements of the LLT that spanned the evaluated frequency spectrum. The lessons learned in this experiment may be useful for others performing INS testing in similar testing facilities.
The co-occurrence of phyllosilicates (clays) and sulfate stratigraphies at many locations on Mars are associated with a sharp change in surface conditions from neutral/alkaline pH during the Noachian (4.1-3.7 Ga) which favored the formation of clays to acidic conditions during the Hesperian (3.7-2.9 Ga) which favored the formation of sulfates. Yet, if these two contrasting geosystems were temporarily sequential, it is unknown how the Hesperian acidic conditions altered the previously formed clay units. We performed laboratory batch experiments to fingerprint the diagnostic features produced during interactions of Mars-analog clays and acidic solutions. Two clays, and silicon (IV) oxide, were reacted with solution of either sulfuric acid or filtered natural acid rock drainage (ARD) in plastic bottles. The solutions were adjusted at four pH values (1, 3, 5, and 7) and reacted at 4, 30, and 80°C for 3, 7, and 14 days. At the end of the experiments, the filtered supernatants were analyzed by ICP-MS while the solids were characterized by X-Ray Diffraction; Energy-Dispersive X-Ray Fluorescence analyses; Raman and Short-Wave Infrared spectroscopies and Scanning Electron Microscopy. Results show that the solution chemistry played a key role in the evolution of the clay-solution systems. In H2SO4 systems, the solution pH steadily increased due to partial clay dissolution with no secondary phase formation detected. Contrary, in the ARD systems, the pH decreased due to the ample presence of Fe which controlled both the reactivity of clays by the growth of protecting surface coatings and the solution pH by the precipitation of Fe nanophases; secondary phase detected included goethite, jarosite, gypsum, and siderite. These results indicate that, on Mars, the chemical interaction between clays and acidic solutions was complex and dependent on the solution chemistry. Acidic, sulfate-rich solutions have a high dissolution capacity and could have induced widespread clay disintegration. Notably, if Fe-rich ARD was involved, the clays on Mars could have remained stable during acidic Hesperian period due to formation of protective coatings. Comparison of our results with martian observations will be performed to determine how acidic conditions could potentially affect clays in sedimentary settings, including the Gale crater.
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Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.