WEBINAR
Collector estimation integrity and delays in exploration stages affect development costs and directly depend on the accuracy and speed of seismic data processing and interpretation algorithms. Join Gazprom Neft and NVIDIA to learn how neural networks label every voxel of a seismic field, marking a massive step towards eliminating uncertainty in petroleum production. In this webinar, the presenters will also demonstrate algorithms on actual field data built with an open source toolkit.
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Sergey Tsimfer leads a team at Gazprom Neft that develops deep learning algorithms for seismic interpretation. Each of the instruments are optimized to utilize resources effectively and can be easily adapted to similar tasks in geological exploration, which greatly accelerates each stage and maximizes GPU utilization. Sergey obtained an MSc of Applied Mathematics at Saint Petersburg State University.

Anna Dubovik and her group at Gazprom Neft have established and implemented standards for data science in the oil and gas community. This includes open tools and data for deep learning engineers in seismic processing and interpretation, benchmarking-ML competitions, hackathons, and online courses. Her previous work was in open source medical AI solutions development for CT and ECG data. Anna obtained an MSc in Machine Learning and Artificial Intelligence at Skoltech University in Moscow.

On the global energy team at NVIDIA, Nefeli Moridis focuses on helping to accelerate subsurface applications, such as reservoir simulation, onto GPUs. Prior to joining NVIDIA, Nefeli worked in the oil and gas industry as a reservoir engineer and as a consultant, focusing on projects in unconventional reservoirs in the U.S. and internationally. Nefeli holds PhD and MSc degrees in Petroleum Engineering from Texas A&M University, a MSc from the French Institute of Petroleum in Reservoir Engineering, and a BSc from the University of Texas at Austin in Petroleum Engineering.
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Date & Time: Wednesday, April 22, 2018