Stanford Doerr School of Sustainability
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Pedro Cintra
Ph.D. Student in Environmental Social Sciences, admitted Autumn 2025
BioTrained as a physicist until my MSc, in which I worked with neutrino detection of core collapse supernovae, I recently switched fields to apply mathematical and computational models to ecological and social systems :)
On the ecological side, I like working with individual based models for cooperation and foraging strategies from an evolutionary perspective. On the social side, I am currently interested in the evolution of cultural values on groups of humans and polarization of opinions on networks of contacts. -
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.
