Rice University Research Repository

The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.


Recent Submissions

Determination of fluid-phase-specific petrophysical properties of geological core for oil, water and gas phases
(2024-03-05) Vinegar, Eva; Singer, Philip M.; Hirasaki, George J.; Chen, Zeliang; Wang, Xinglin; Vinegar, Harold J.; Rice University; Vinegar Technologies LLC; United States Patent and Trademark Office
The following invention is used for determining the relative permeability of a fluid in a rock for three different phases: water, oil, and gas, in both conventional and unconventional formations. The permeability of a phase describes how much it can flow in porous media given a pressure gradient and is useful in evaluating reservoir quality and productivity. The following invention is a method to determine the three-phase relative permeabilities in both conventional and unconventional formations using NMR restricted diffusion measurements on core with NMR-active nuclei, combined with centrifugation of the core. In addition, the tortuosity, pore size (surface-to-volume ratio), fluid-filled porosity, and permeability is determined for each of the three phases in a rock.
Partitioned machine learning architecture
(2024-03-05) Rouhani, Bita Darvish; Mirhoseini, Azalia; Koushanfar, Farinaz; Rice University; United States Patent and Trademark Office
A system may include a processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: partitioning, based at least on a resource constraint of a platform, a global machine learning model into a plurality of local machine learning models; transforming training data to at least conform to the resource constraint of the platform; and training the global machine learning model by at least processing, at the platform, the transformed training data with a first of the plurality of local machine learning models.
Method of estimating permeability using NMR diffusion measurements
(2024-03-19) Vinegar, Harold J.; Singer, Philip M.; Hirasaki, George J.; Chen, Zeliang; Wang, Xinglin; Vinegar, Eva; Rice University; Vinegar Technologies LLC; United States Patent and Trademark Office
This invention is useful for determining the permeability of a geological formation using 1H NMR diffusion measurements acquired in the laboratory and using downhole 1H NMR well logging. The current technology for obtaining formation permeability downhole using NMR is not adequate for low-permeability, unconventional source rock formations with high organic content. This new method uses laboratory 1H NMR diffusion measurements for creating continuous downhole well logs of the mobile-hydrocarbon permeability of the hydrocarbon-filled pore space of downhole geological formations.
Research Data Services and Center for Research Computing Needs Assessment
(Rice University, 2024-03-25) Barber, Catherine R.; Spiro, Lisa; Cragin, Melissa; Smith, Sean; The Center for Research Computing; Fondren Library Research Data Services
This needs assessment involved surveying the Rice University research community to determine key areas of current and projected need related to data and research computing. The results will be used to inform services and support provided by Fondren Library, Research Data Services, and the Center for Research Computing. Although the results of the needs assessment may not generalize outside of Rice, we also hope to learn about the opportunities for interdisciplinary collaboration between the library and research computing and to share those insights with the greater research community. Data were collected through an online survey administered to all faculty, research staff, and graduate students at Rice.
Shakespeare Passages Recommendation System
(Rice University) Mulligan, John; Center for Research Computing
This repository holds the code for an intertextual recommendation system that links passages in the Shakespearean dramatic corpus (as digitized by Folger) to one another based entirely on scholarly citations/quotations (as identified by JSTOR Labs in their collection of digitized works, and made available in what was called their Matchmaker API).