Post-doc Position in Department of Energy Resources Engineering Stanford University Optimization of Oil Field Development We seek a post-doc for research focusing on the development and application of new algorithms for oil field optimization. The project will target the development of procedures for optimizing the location and type of new wells, determining the optimal well pattern, optimizing the drilling sequence, and accounting for geological and engineering uncertainty. Current approaches under study by our group include the use of evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) coupled to large-scale reservoir flow simulators. Applicants for this position should have research experience in simulation-based optimization and/or oil reservoir simulation. The project is funded by the Italian energy company ENI. The work will entail both algorithm development and application of the algorithms to field problems defined by ENI. Some travel to Milan will be required, but the significant majority of the work will be conducted at Stanford. The funding will start in early 2009 and is anticipated to last for two years. Interested applicants should send (1) a curriculum vita with a complete list of publications, (2) names of three references including email addresses, and (3) copies of relevant papers to: Dr. David Echeverria Ciaurri Department of Energy Resources Engineering, Stanford University email: echeverr@stanford.edu This search will proceed until a suitable candidate is selected. Please see http://pangea.stanford.edu/ERE/index.php for information on the Department of Energy Resources Engineering at Stanford. Stanford University has a strong institutional commitment to the principle of diversity. In that spirit, we particularly encourage applications from women, members of ethnic minorities, and individuals with disabilities.

The Stanford Smart Fields Consortium is a multidisciplinary industrial affiliates program with participants from several departments of Stanford University. The research there involves the development and testing of optimization, risk assessment, decision-making and real-time monitoring of model calibration techniques, within the oil industry.