معرفی
Susan Schrader serves as an Associate Professor in the Department of Petroleum Engineering within Montana Technological University's Lance College of Mines & Engineering, where she has held faculty positions since 2008 after previously teaching at the University of Texas Permian Basin. Her academic foundation includes a PhD in Petroleum Engineering from the New Mexico Institute of Mining and Technology and an MA in Applied Mathematics from the University of New Mexico, with additional doctoral coursework in Mathematics at Montana State University.
Her educational qualifications comprise:
- PhD in Petroleum Engineering, New Mexico Institute of Mining and Technology
- MA in Applied Mathematics, University of New Mexico
- ABD toward PhD in Mathematics, Montana State University
Schrader's research program centers on petroleum engineering with dual emphases on technical innovation and educational advancement. She develops computational models for fluid flow in low-permeability reservoirs while pioneering data analytics and machine learning applications for reservoir simulation. Concurrently, she investigates climate change mitigation through carbon sequestration and examines societal dimensions of energy industry-climate relationships. Her educational scholarship focuses on accreditation, program assessment, and promoting diversity within STEM disciplines, reflecting commitment to both technical excellence and inclusive pedagogy.
Publication analysis reveals an evolutionary trajectory from early fuzzy logic applications in drilling risk assessment toward contemporary integration of neural networks in reservoir engineering and socio-technical climate research. This progression demonstrates increasing sophistication in computational methodologies while expanding into interdisciplinary energy-environment interfaces, particularly evident in her recent low-permeability flow modeling and climate change studies.
No scientific awards are documented in available sources.
Schrader's instructional responsibilities span advanced computational courses including Data Modeling for Petroleum Engineers, Data Science, and Advanced Reservoir Engineering, indicating substantial curriculum development contributions. While specific graduate student mentorship details are absent from provided materials, her research areas suggest supervision of projects combining petroleum engineering with data science methodologies. Grant funding specifics remain undisclosed in the source documentation.
Her computational research likely leverages Montana Tech's High Performance Computing Cluster infrastructure, though no dedicated laboratory facilities are explicitly attributed to her in the available information.
