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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)

Here, artificial neural network-trained models were used to predict gas hydrate saturation distributions in permafrost-associated deposits in the Eileen Gas Hydrate Trend on the Alaska North Slope (ANS), USA and at the Mallik research site in the Beaufort-Mackenzie Basin, Northwest Territories, Canada. The database of Logging-While-Drilling (LWD) and wireline logs collected at five wells (Mount Elbert, Ignik Sikumi, and Kuparuk 7–11–12 wells at ANS, plus 2L-38 and 5L-38 wells at the Mallik research site) includes more than 10,000 depth points, which were used for training, validation, and testing the machine learning (ML) models. Data used in training the ML models include the well logs of density, porosity, electrical resistivity, gamma radiation, and acoustic wave velocity measurements. Combinations of two or three out of these five well logs were found to reliably predict the gas hydrate saturation with accuracy varying between 80 and 90% when compared to the gas hydrate saturations derived from Nuclear Magnetic Resonance (NMR)-based technique. The ML models trained on data from three ANS wells achieved high fidelity predictions of gas hydrate saturation at the Mallik site. The results obtained in this study indicate that ML models trained on data from one geological basin can successfully predict key reservoir parameters for permafrost-associated gas hydrate accumulations within another basin. A generalized approach for selecting a well log combination that can improve model accuracy is discussed. Overall, the study outcome supports earlier work demonstrating that ML models trained on non-NMR well logs are a viable alternative to physics-driven methods for predicting gas hydrate saturations.

58 GEOSCIENCES↗
DOE OSTI2021

Evidence of an oxidation induced phase transformation for a delta phase plutonium-gallium alloy

The oxidation of an ~1.5 at% Ga plutonium metal alloy in dry air as a function of time was studied using a combination of FIB-SEM, AES, and XRD. Analysis of SEM images of FIB cross-sections of the oxide allowed for direct measurement of the oxide thickness as well as determination of heterogeneity in the extent of the oxide. AES analysis performed on cross-sections of the oxide allowed for quantitative analysis of the atomic composition and semi-quantitative analysis of the chemical composition. Initial preparation of the sample was found to induce a martensitic transformation to α' - Pu with an average thickness of 233 nm. Electropolishing was found to be sufficient to fully remove the α' layer with creation of only a minimum oxide layer. Growth of the oxide in dry air was found to initially follow parabolic kinetics, with a parabolic rate constant orders of magnitude higher than expected for δ-Pu but in-line with values extrapolated to room temperature for α-Pu. Following complete oxidation of the initial α` layer, further regions of plutonium metal depleted in gallium were observed to form at the oxide / metal interface. However, gallium, as Ga 2 O 3 , was found to be enriched at the surface and present in the oxide in an amount comparable to the relative Ga:Pu ratio of the bulk metal alloy. Finally, the results presented here serve to bridge a diverse array of previous experimental and theoretical data and provide the first comprehensive view of the fate of the δ-stabilizer during formation of an oxide.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-species collisions for delta-f gyrokinetic simulations: Implementation and verification with GENE

we report a multi-species linearized collision operator based on the model developed by Sugama et al. has been implemented in the nonlinear gyrokinetic code, GENE. Such a model conserves particles, momentum, and energy to machine precision, and is shown to have negative definite free energy dissipation characteristics, satisfying Boltzmann’s H-theorem, including for realistic mass ratio. Finite Larmor Radius (FLR) effects have also been implemented into the local version of the code. For the global version of the code, the collision operator has been developed to allow for block-structured velocity space grids, allowing for computationally tractable collisional global simulations. The validity of the collision operator has been demonstrated by relaxation and conservation tests, as well as appropriate benchmarks. The newly implemented operator shall be used in future simulations to study magnetically confined fusion plasma turbulence and transport in more extreme regions with higher collisionality.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

The large decline in carbonate $\delta^{238}$U from a PETM section at Tingri (South Tibet) was driven by local sea-level changes, not global oceanic anoxia

Uranium isotope compositions (δ 238 ) and Th/U in carbonates are being explored as paleoredox proxies to study global oceanic anoxia. However, the impact of changes to local depositional environments that might cause a decline in δ 238 U and an increase in Th/U has not been thoroughly investigated. Here we report a decline in δ 238 U (from +0.1 to -0.3‰) and an increase in Th/U from shallow-marine carbonates during the Paleocene-Eocene thermal maximum at Tingri (south Tibet). Changes of δ 238 U and Th/U are not fully coupled. The decline in δ 238 U coincided with a local sea-level fall, whereas the increase in Th/U occurred mainly during a sea-level rise. The decline in δ 238 U likely results from changes in redox conditions of pore waters and in primary carbonate mineralogy. The increase in Th/U is ascribed to reduced authigenic U(IV) accumulation. Here our results suggest that changes in local depositional environments can cause behavior of δ 238 U and Th/U resembling that induced by expanded global oceanic anoxia. This finding calls for caution to consider local factors before applying δ 238 U and Th/U as global paleoredox proxies.

58 GEOSCIENCES↗