Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Adult and Continuing Education (1)
- Curriculum and Instruction (1)
- Data Science (1)
- Earth Sciences (1)
- Education (1)
-
- Educational Administration and Supervision (1)
- Educational Leadership (1)
- Elementary and Middle and Secondary Education Administration (1)
- Engineering (1)
- Engineering Science and Materials (1)
- Higher Education (1)
- Higher Education Administration (1)
- Life Sciences (1)
- Operations Research, Systems Engineering and Industrial Engineering (1)
- Physics (1)
- Secondary Education (1)
- Secondary Education and Teaching (1)
- Social and Behavioral Sciences (1)
- Statistics and Probability (1)
- Systems Engineering (1)
- Teacher Education and Professional Development (1)
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh
Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh
Faculty & Staff Scholarship
Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …
Enabling And Threatening Factors Affecting Persistence. A Qualitative And Quantitative Study On Rural First-Generation Stem Students’ And Stem Faculty's Perspectives., Travis A. Miller
Graduate Theses, Dissertations, and Problem Reports
This study focuses on the factors that enable and threaten rural first-generation STEM students’ persistence. Limited empirical studies are available that focus on rural first-generation STEM majors’ persistence. Quantitative analysis was conducted using Kruskal Wallis H and Mann-Whitney U tests to determine any significant differences with the survey results. Content and thematic analysis was conducted on the student and faculty interviews to determine themes of enabling and threatening factors affecting persistence.
Enabling factors affecting persistence were found to be: Drive or Motivation, Experiences and skills, and Support. These were both faculty and student interview themes whereas a …