Machine Learning (ML) is a field of Artificial Intelligence that has experienced rapid growth in the last ten years across diverse industries, including communications, financial services, security, transportation, and others. Applications of ML have produced dramatic results, enabling new opportunities and business models. Driving the adoption of ML 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. ML facilitates an understanding of complex relationships among a large and diverse set of variables, valuable for generating and validating models and answering scientific questions. ML can enable fast high-quality decisions in the oil & gas industry, an essential component for viability given the industrys 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. The workshop will allow the attendees to evaluate and compare the best developments of ML technology that have occurred in industry over the last five years.
Good-Morning and welcome to: 4th Joint SBGf/SEG Workshop on Machine Learning.
A Workshop is a great opportunity to combine theory with practical activities, and with the help of mediators, experts, and training produced by SBGf, participants acquire skills and make the most of everything the universe of knowledge has to offer. In the Workshop 4th Joint SBGf/SEG Workshop on Machine Learning, you will have a great opportunity to learn and put your knowledge into practice.
This in-person workshop will discuss the challenges, opportunities, and trends related to the adoption of Machine Learning in geoscience research and industrial applications. Professionals from academia, Oil & Gas, Mining, and technology companies from different areas will present applications and case studies, promote discussion, and propose practical solutions to take greater advantage of Machine Learning methods.
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