The Stanford BPSM program provides expertise in visualization and quantification of the geohistory of sedimentary basins and petroleum systems. The primary objectives are to train the next generation of basin and petroleum system modelers, devise quantitative tools that can be used to rigorously evaluate geologic risk in various exploration settings, and conduct basic and applied energy-focused research.
Founding of BPSM
Our industrial affiliates program was developed by a team of Stanford geoscientists in order to train students in quantitative modeling of sedimentary basins and petroleum systems. The program was developed in 2008 with the cooperation and support of the Department of Geological & Environmental Science (now the Department of Geological Sciences) and the the Department of Energy Resources Engineering within the School of Earth, Energy & Environmental Sciences, with the Department of Geophysics joining later.
BPSM will use and develop open-source software in the form of packaged scripts, and it is the intention of BPSM that these packages will be released under an open source model via GitHub. Presentations, information, data, and results will be shared with all members and the public upon request.
What We Study
Over the last decade, three-dimensional (3-D) imaging and modeling of the subsurface through time (4-D) have co-evolved and emerged as a major research focus of the petroleum industry because of the need to organize data and archive data, visualize geologic processes, communicate with stakeholders, and convert static data into dynamic processed data and interpretations.
Virtually all major oil companies have independently recognized the need for 4-D petroleum system models (also called basin models) because they:
* Organize data, allowing deficiencies or inconsistencies to be identified
* Archive data (data loss due to personnel attrition and reorganization is a major cost factor)
* Facilitate visualization of geologic processes and communication with stakeholders and
* Add value by converting static data into dynamic processed data and interpretations.