Petroleum Systems
Reservoir characterization, production context, field reporting, and petroleum management models.
R-01 / Research
Research.
Using machine learning, statistics, computer science, and software engineering to advance petroleum, CCS/CCUS, and energy science.
R-02
Research Mission
Applied research for subsurface problems where model behavior, geological context, and operating constraints need to stay connected.
Scope
Methods for energy systems.
Intelligent Geologics studies how computational methods can support petroleum management, carbon storage, and subsurface energy programs. The work connects modeling, data systems, and software tools that can be reviewed by engineers.
R-03
Application Areas
CCS and CCUS
Storage forecasting, pressure behavior, plume migration, surveillance planning, and injection program design.
Energy Science
Subsurface workflows for geothermal, unconventional resources, and adjacent energy systems.
R-04
Methods
Models, statistics, data pipelines, and product surfaces stay part of the same research loop.
Machine Learning
Surrogate models, forecasting workflows, uncertainty handling, and pattern recognition for subsurface systems.
Statistics
Sparse-data modeling, comparative evaluation, constraint handling, and decision analysis.
Computer Science and Software Engineering
Research infrastructure, experiment tracking, data pipelines, and software tools for technical handoff.
R-05
Latest Research
A place for technical notes, working papers, and public research updates.
Forthcoming
Research will be published here.
Public write-ups are forthcoming. Future notes will cover machine learning, statistical modeling, and software systems for petroleum, CCS/CCUS, and adjacent energy science.