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Qin, Yan

Publications and source records attributed to Qin, Yan.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Intraplate Stress Field of West Africa

West Africa continues to host a growing number of low and intermediate-magnitude earthquakes (M2-5) along its passive margins, and its continental interior. Earthquake activity in these regions raises the need to comprehend the causes and the tectonic controls of the seismicity. Unfortunately, such studies are rare. Here, we apply single-station inversion techniques to constrain fourteen focal mechanisms, computed after compiling a set of high-quality waveforms. We describe the connection between seismicity, the contemporary stress field, anthropogenic activity and Holocene fault scarps in the region. Our results indicate transpressive stresses acting on the inherited brittle structures in the passive margins. We also observe a compressive regime in the intracontinental failed rifts. We attribute the seismicity to the reactivation of “weak” faults in the Neoproterozoic and Mesozoic failed rifts, the passive transform structures, and the intracratonic Precambrian brittle shear zones.

58 GEOSCIENCES↗
DOE OSTI2024

Suppressing strain propagation in ultrahigh-Ni cathodes during fast charging via epitaxial entropy-assisted coating

Surface reconstruction and the associated severe strain propagation have long been reported as the major cause of cathode failure during fast charging and long-term cycling. Despite tremendous attempts, no known strategies can simultaneously address the electro-chemomechanical instability without sacrificing energy and power density. Here we report an epitaxial entropy-assisted coating strategy for ultrahigh-Ni LiNi x Co y Mn 1-x-y O 2 (x ≥ 0.9) cathodes via an oriented attachment-driven reaction between Wadsley-Roth phase-based oxides and the layered-oxide cathodes. Further, the high anti-cracking and anti-corrosion tolerances as well as the fast ionic transport of the entropy-assisted surface effectively improved the fast charging/discharging capability, wide temperature tolerance and thermal stability of the ultrahigh-Ni cathodes. Comprehensive analysis from the primary and secondary particle level to the electrode level using multi-scale in situ synchrotron X-ray probes reveals greatly reduced lattice dislocations, anisotropic lattice strain and oxygen release as well as improved bulk/local structural stability, even when charging beyond the threshold state of charge (75%) of layered cathodes. Layered Ni-rich oxide cathodes are susceptible to challenges with surface reconstruction and strain propagation, limiting their cyclability. The authors propose a solution involving oriented attachment-driven reactions, utilizing Wadsley-Roth nanocrystals and layered oxide to induce an epitaxial entropy-assisted coating, effectively addressing these issues.

25 ENERGY STORAGE↗
DOE OSTI2023

Critical Contribution of Imbalanced Charge Loss to Performance Deterioration of Si-Based Lithium-Ion Cells during Calendar Aging

Increasing the energy density of lithium-ion batteries, and thereby reducing costs, is a major target for industry and academic research. One of the best opportunities is to replace the traditional graphite anode with a high-capacity anode material, such as silicon. However, Si-based lithium-ion batteries have been widely reported to suffer from a limited calendar life for automobile applications. Heretofore, there lacks a fundamental understanding of calendar aging for rationally developing mitigation strategies. Both open-circuit voltage and voltage-hold aging protocols were utilized to characterize the aging behavior of Si-based cells. Particularly, a high-precision leakage current measurement was applied to quantitatively measure the rate of parasitic reactions at the electrode/electrolyte interface. The rate of parasitic reactions at the Si anode was found 5 times and 15 times faster than those of LiNi 0.8 Mn 0.1 Co 0.1 O 2 and LiFePO 4 cathodes, respectively. Here, the imbalanced charge loss from parasitic reactions plays a critical role in exacerbating performance deterioration. In addition, a linear relationship between capacity loss and charge consumption from parasitic reactions provides fundamental support to assess calendar life through voltage-hold tests. These new findings imply that longer calendar life can be achieved by suppressing parasitic reactions at the Si anode to balance charge consumption during calendar aging.

25 ENERGY STORAGE↗
DOE OSTI2023

A Li-rich layered oxide cathode with negligible voltage decay

With high capacity at low cost, Li- and Mn-rich (LMR) layered oxides are a promising class of cathodes for next generation Li-ion batteries. However, substantial voltage decay during cycling, due to the unstable Li 2 MnO 3 honeycomb structure, is still an obstacle for their practical deployment. Here we report a Co-free LMR Li-ion battery cathode without voltage decay. The material has a composite structure consisting of layered LiTMO 2 and various stacked Li 2 MnO 3 components, where transition metal (TM) ions partially reside in the Li layers of Li 2 MnO 3 , serving as a “cap” to strengthen the stability of the honeycomb structure. This unique capped-honeycomb structure is persistent after high-voltage cycling and prevents TM migration and oxygen loss, as shown by experimental and computational results. Furthermore, this work demonstrates that the long-standing voltage decay problem in LMRs can be effectively eliminated by internally pinning the honeycomb structure, which opens an avenue to developing nextgeneration high-energy cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Microseismic Monitoring at the Farnsworth CO 2 -EOR Field

The Farnsworth Unit in northern Texas is a field site for studying geologic carbon storage during enhanced oil recovery (EOR) using CO 2 . Microseismic monitoring is essential for risk assessment by detecting fluid leakage and fractures. We analyzed borehole microseismic data acquired during CO 2 injection and migration, including data denoising, event detection, event location, magnitude estimation, moment tensor inversion, and stress field inversion. We detected and located two shallow clusters, which occurred during increasing injection pressure. The two shallow clusters were also featured by large b values and tensile cracking moment tensors that are obtained based on a newly developed moment tensor inversion method using single-borehole data. The inverted stress fields at the two clusters showed large deviations from the regional stress field. The results provide evidence for microseismic responses to CO 2 /fluid injection and migration.

02 PETROLEUM↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2022

Influence of Fault Architecture on Induced Earthquake Sequence Evolution Revealed by High-Resolution Focal Mechanism Solutions

The increasing seismicity and improved seismic observation network in recent years provide an opportunity to explore factors that influence the triggering processes, spatiotemporal evolution, and maximum magnitude of induced sequences. We map the fault architecture and stress state of four induced sequences in Oklahoma to determine their influence on the seismicity. We systematically relocate the earthquakes and compute hundreds of focal mechanisms of small to medium events (1.0 < M < 5.1) using various techniques, including machine learning, for the Guthrie, Woodward, Cushing, and Fairview sequences. The detailed fault geometry and spatiotemporal evolution of seismicity and stress states reveal different dominant driving forces for each sequence. In Cushing and Fairview (largest event ≥M5.0), the main fault structures are near-vertical narrow strike-slip faults, with most of the small earthquake fault planes optimally oriented. The two sequences exhibit discontinuous temporal migration but strong earthquake self-driven rupture growth. In Guthrie and Woodward (largest event <M5.0), the two sequences show more complex diffuse fault structures with varying dipping angles along depth. The inverted focal mechanisms show a mix of strike-slip faulting and normal faulting in both sequences, and the normal faulting events are less optimally oriented than strike-slip events. The two sequences are dominated by continuous diffusive migration in time driven by pore pressure propagation. The above results suggest that fault architecture and stress state influence sequence evolution, major driving forces, and possibly maximum magnitude.

58 GEOSCIENCES↗
DOE OSTI2022

Enable High-Energy LiNi0.5Co0.2Mn0.3O2 by Ultra-Thin Coating through Wet Impregnation

A high cut-off voltage is required for nickel-rich layered oxide LiNixCoyMnzO2 (NCM) to meet the high energy density requirement of lithium-ion batteries in electric vehicles. However, such a high voltage application leads to an unstable interface between NCM and liquid electrolytes. To stabilize the interface, the facile wet impregnation method has been developed to apply an ultra-thin Al2O3 coating layer on the NCM particles. This coating layer was found to have a strong interaction with the NCM and resulted in Al-doped NCM at the surface structure of NCM. The change of surface structure can not only reduce the surface resistance of lithium diffusion of LiNi0.5Co0.2Mn0.3O2 (NCM523), but also stabilize the solid electrolyte interface between NCM523 and the electrolyte with the cut-off voltage of 4.5 V vs. Li/Li+. Compared to other coating methods, wet impregnation coating can provide an ultra-thin and uniform coating with surface doping on NCM particles. Furthermore, this scalable coating method can be applied to various electrode materials without adding much additional cost.

25 ENERGY STORAGE↗
DOE OSTI2022

Forecasting induced seismicity in Oklahoma using machine learning methods

Oklahoma earthquakes in the past decade have been mostly associated with wastewater injection. Here we use a machine learning technique—the Random Forest to forecast induced seismicity rate in Oklahoma based on injection-related parameters. We split the data into training (2011.01–2015.05) and test (2015.06–2020.12) periods. The model forecasts seismicity rate during the test period based on input features, including operational parameters (injection rate and pressure), geological information (depth to basement), and modeled pore pressure and poroelastic stress. The results show overall good match with observed seismicity rate (adjusted R 2 of 0.75). The model shows that pore pressure rate and poroelastic stressing rates are the two most important features in forecasting. The absolute values of pore pressure and poroelastic stress, and the injection rate itself, are less important than the stressing rates. These findings further emphasize that temporal changes of stressing rates would lead to significant changes in seismicity rates.

58 GEOSCIENCES↗