Quantitative Numerical Modeling of Petroleum Systems
A team of researchers has developed a science plan to train students using active research in quantitative numerical modeling of petroleum systems through an industrial affiliates program at Stanford University. The plan was developed with the cooperation and support of the School of Earth, Energy & Environmental Sciences, including the Department of Geological Sciences and the new Center for Computational Earth & Environmental Science (CEES) at Stanford University.
SCRF was initiated in 1988 to further the development of techniques for forecasting reservoir performance and for integrating geological, geophysical and reservoir engineering data. The SCRF group performs paradigm-changing research in the field of geostatistics and numerical reservoir modeling. We are not bound by the limited extent of project-based research with its short-term deadlines and limited scope. This long-term perspective has lead to revolutionary changes in reservoir modeling, amongst which: the introduction of stochastic simulation in reservoir modeling, GSLIB as a standard geostatistical software package, the advent of multiple-point geostatistics, practical solutions for large-scale inverse problems with geological constraints, an open-source software termed S-GEMS, and techniques for modeling uncertainty. The funding mechanism of SCRF has created a long-term think-tank where a group of faculty, post-doctoral researchers, graduate students, visiting scholars and industry experts come together to tackle problems of first-order importance in quantitative modeling of space-time varying phenomena and their applications in reservoir modeling.
The Stanford Project on Deepwater Depositional Systems (SPODDS) is a research program focused on the study of ancient and modern coarse-clastic deep-water deposits from around the world. Affiliate members of this industrial consortium include numerous international energy companies that seek greater understanding of deep-water deposits as reservoir system for oil and gas.