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Joint SBGf/SEG Workshop on Machine Learning

15-16 May 2018 | Rio de Janeiro, Brazil


15-16 May 2018
Rio de Janeiro, Brazil

Machine Learning (ML) is a field of Artificial Intelligence (AI) that has experienced rapid growth in the last ten years across diverse industries, including communications, financial services, security, transportation, and others. Applications of Machine Learning have produced dramatic results, enabling new opportunities and business models. Driving the adoption of Machine learning are the volume and velocity of information, the application of deep learning techniques, and economic computing power. Applied to geoscience, these data-driven approaches are complementary tools for physical-based modeling, simulation, and inversion. Machine Learning facilitates an understanding of complex relationships among a large and diverse set of variables, valuable for generating and validating models and answering scientific questions. Machine Learning can enable fast high-quality decisions in the Oil & Gas industry, an essential component for viability given the industry’s long-term outlook. Geoscience datasets are among the largest volumes of data in the industry.  The data has a wide spectrum of properties with scales varying over many orders of magnitude. This workshop will discuss the challenges, opportunities, and trends related to the adoption of Machine Learning in Geoscience research and industrial workflows. Professionals from academia, Oil & Gas, and technology companies will present applications and case studies, promote discussion, and propose practical solutions to take greater advantage of Machine Learning methods.


Golden Tulip Hotel
Copacabana, Rio de Janeiro

General Co-Chairs

  • Pedro Mário Cruz e Silva (NVIDIA)
  • Klaus Soffried (Geophysical Insights)


SBGf Events Coordinator
Renata Vegasta

SEG Event Planner
Jill Abbott

Technical Program

The call for abstracts closed 30 January 2018.

Suggested Topics

  • Applications to geophysics
  • Case studies in Geoscience
  • Case studies in reservoir characterization and management
  • Future Trends

Technical Committee

  • Elita Abreu (Petrobras)
  • Lucas Balancin (Petrobras)
  • Gregori Fabre (Total)
  • Hal Green (Geophysical Insights)
  • Matt Hall (Agile)
  • Paulo Johann (Petrobras)
  • Marcilio Matos (SISMO Signal Processing Research, Training & Consulting)
  • Carlos Rodriguez (Independent Consultant)
  • Yang Xue (Shell)

Download the Call for Abstracts (PDF)

Important Documents

Important Dates

Call for Abstracts closed
30 January 2018

Early registration closes
15 April 2018

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