Stanford Doerr School of Sustainability
Showing 1-4 of 4 Results
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Catherine (Hay) Callas
Ph.D. Student in Energy Resources Engineering, admitted Spring 2020
BioCatherine Callas is a Ph.D. candidate in the Benson Lab in Energy Resources Engineering. She is an ExxonMobil Emerging Energy Fellow, and her research is focused on offshore carbon capture and sequestration in the Gulf Coast. She obtained her M.S. degree in the Atmosphere and Energy program within Civil and Environmental Engineering from Stanford University and a B.S. degree in Chemical Engineering from Brown University. Before attending Stanford, she worked as a Financial Analyst within the Fixed Income group at Goldman Sachs in New York City for three years. She was a Schneider Fellow at the Natural Resources Defense Council in San Francisco where she analyzed the impact of the 2017 Northern California wildfires and 2018 Camp Fire on retail rates within PG&E’s service territory.
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Zhenlin Chen
Ph.D. Student in Energy Resources Engineering, admitted Summer 2023
BioZhenlin (Richard) Chen is a Ph.D. candidate at Stanford's Adam Brandt lab, focuses on greenhouse gas emissions from oil and gas. His work primarily revolves around evaluating ground sensor technologies for methane detection and quantification ability. His methodological approach blends engineering principles, field data collection, and applied statistics. Chen is exploring AI-driven frameworks, particularly large language models, to refine energy data extraction and enhance the OPGEE model through private data fine-tuning and reinforcement learning. His emphasis remains on domain-specific tasks, aiming for efficiency in terms of latency and cost. He pursued his undergraduate studies in environmental science at Cornell University and holds a master's in Atmosphere and Energy Engineering from Stanford.
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Dylan Marshall Crain
Ph.D. Student in Energy Resources Engineering, admitted Autumn 2022
Current Research and Scholarly InterestsMy current research revolves around optimizing the monitoring design of Carbon Capture and Sequestration (CCS) projects in such a way that the posterior (after data assimilation) predictions are as close to reality as can be hoped for.
In CCS projects within the U.S., it is important to have monitoring plan, which can consist of wells with pressure, saturation, salinity, et cetera sensors, seismic lines, or gravimetric above-ground measurements, before any injection has begun into the subsurface. This is due to the permitting requirements that must be satisfied before operations are begun.
Due to this constraint, any monitoring optimization (at least initially) needs to be determined using only a prior (highly uncertain) understanding of the subsurface. This makes the optimization much more challenging. We utilize a prior optimization scheme from a previous student which allows us to optimize a monitoring plan using only prior information to get the minimized, expected uncertainty reduction in the posterior models for a given quantity of interest. This scheme is limited by some Gaussian assumptions. We optimize it using a genetic algorithm.
From this point, with the monitoring plan established, the information gathered from the optimized monitoring scheme (using only monitoring wells at the moment) is used to history match (data assimilate) our understanding of the subsurface. The results can be used to predict the CO2 plume flow and behavior into the future.
This work was initially developed to assist a project in Illinois that is currently seeking Class VI injection well permits in the self-same state in order to begin injecting CO2 produced from two companies paying for the work from the Illinois Geological Survey.
