Representatives of big tech and oil & gas will share how machine learning, cloud computing, GPU computing, and the industrial internet of things (IIoT) will re-shape how hydrocarbons are found and monetized. The event kicks off with a networking breakfast followed by a plenary session with an outstanding slate of speakers and topics, including:
John Adamick, senior V.P., data and analytics | TGS
Dr. Sumit Gupta, V.P., AI and machine learning | IBM
Satyen Yadav, general manager – machine learning | Amazon
Dr. Mauricio Araya, senior researcher, computer science | Shell
Dr. Tom Smith, president and CEO | Geophysical Insights
Breakout sessions on machine learning technology, machine learning applications, and the future of geoscience will be available during the afternoon. And, an engaging panel discussion on The Impact of Machine Learning on Geoscientists will follow the breakout sessions. A networking reception is the capstone of the event where speakers and attendees will have opportunities mix and to discuss the day’s presentations.
“We are very excited to have the Society of Exploration Geophysicists (SEG) and the Geophysical Society of Houston, along with other sponsors, to help champion and shape the future of machine learning in oil & gas,” said Dr. Tom Smith, president and CEO of Geophysical Insights. “Geophysical Insights is fully invested in the development of machine learning for interpretation, and we are delighted to help host this event for the industry.
"The SEG is proud to support the 2018 Oil & Gas Machine Learning Symposium,” according to SEG President Nancy House. "While the SEG is hosting workshops worldwide, this event is an excellent complement to the SEG Annual Meeting in Anaheim, October 14-19, 2018, and will be particularly appealing to the leadership of E&P companies who seek a better understanding of the benefits and challenges posed by machine learning in geophysical interpretation, acquisition design, and data processing to help shorten the cycle and enable geophysical technology to increase its application to E&P problems."