The Andrei Borisovich Vistelius Research Award : Pejman Tahmasebi
Pejman Tahmasebi holds B.Sc. and M.Sc. degrees from Tehran Polytechnic. For his Ph.D., he worked under the supervision of Professor Muhammad Sahimi of the University of Southern California (USC) on modeling of oil reservoirs and porous media. He completed his Ph.D. in 30 months, publishing several papers in Physical Review Letters, Physical Review E, and Computational Geosciences.
He joined the Stanford Center for Reservoir Forecasting as a research fellow and worked on rapid reservoir modeling and updating. This was followed by appoints to research scientist positions at the University of Texas at Austin and USC (development of novel techniques for modeling of shale reservoirs) and finally at the Department of Mechanical and Civil Engineering of the California Institute of Technology (Caltech) where he worked on granular materials. Tahmasebi is currently an Assistant Professor at the Department of Petroleum Engineering at the University of Wyoming.
Tahmasebi’s research interests include multiple-point geostatistics, porous media reconstruction and data integration. He has published 38 peer-reviewed journal papers and two book chapters on topics related to modeling of porous media, both at laboratory and field scales, data mining, and reconstruction of various types of disordered media and materials.
Multiple Point Statistics for Reservoir and Porous Media Modeling
The purpose of any reconstruction method is to generate realizations of two- or multiphase disordered media that honor limited data for them, with the hope that the realizations provide accurate predictions for those properties of the media for which there are no data available, or their measurement is difficult. Geostatistical methods are used widely in the earth sciences for the reconstruction purpose. Most of the traditional methods rely on two-point covariance, which has been proven to be insufficient to capture the complexity in the earth sciences. Recently, multiple point statistical methods have received more attention. The concept is to first present the prior conceptual information through an image. Then, small pieces of the patterns in the image are stochastically anchored to the available dataset. In this talk, I will present the latest algorithm that addresses the complexity which is able to be used for accurate modeling of very large oil/gas reservoirs and small-scale porous media.
The Felix Chayes Prize for Excellence in Research in Mathematical Petrology: Cliff Stanley
Cliff Stanley received his B.A. degree (1980) in Earth Sciences from Dartmouth College, and his M.Sc. (1984) and Ph.D. (1988) degrees in Geological Sciences from the University of British Columbia under the supervision of Dr. Alastair Sinclair. After a post-doctoral fellowship in 1989 with Drs. Ed Ghent and Jim Nicholls in numerical petrology at the University of Calgary, Cliff became a research associate with Dr. Ian Nicol in applied geochemistry at Queen’s University (1990-1991), before joining the Mineral Deposit Research Unit (UBC) as an adjunct professor (1992-1998). There he managed major porphyry Cu-Au and lithogeochemical exploration research projects. In 1999, Cliff became an assistant professor in applied geochemistry/economic geology at Acadia University (Wolfville, Nova Scotia), and is presently a full professor in their Department of Earth and Environmental Science.
Cliff’s research interests have largely involved numerical applications in geochemistry and petrology, and he has authored several publications illustrating the application of molar element ratio analysis to studies of rock geochemistry. In addition, Cliff has made important contributions to the quantitative treatment of sampling and analysis error in exploration and mining samples.