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The Upstream Takes AI from Theory to Practice at the 2019 Oil and Gas Machine Learning Symposium

Houston, TX, October 02, 2019 –(– Over the past several years, AI has grown in relevance and adoption within the upstream oil and gas community. With the launch of several major projects this year by the likes of ExxonMobil, BP and Total, 2019 is being hailed as the year AI goes from theory to practice.

Delegates will gather on 30 October at the Royal Sonesta in Houston, Texas to discuss the business and technology driving their respective digital transformation initiatives, and the impact on their organizations. This year’s lineup of speakers includes AI pioneers from Apache, ConocoPhillips, ExxonMobil, Shell, Dell, IBM, TGS and others.

Following the sold-out inaugural event in 2018, the 2019 Oil and Gas Machine Learning Symposium will highlight machine learning, deep learning, and other AI applications. Thought leaders, innovators, and researchers from across the upstream oil and gas sector will share lessons learned, best practices, and development direction. The event is becoming a destination among data scientists, geoscientists, engineers, asset managers, and exploration managers.

Breakout sessions are aligned along four major themes that all have profound implications for the oil and gas business. Technical and business leaders will present their latest findings and case studies in the following areas:

· Exploration and Development Machine Learning Applications
· Reservoir Engineering Machine Learning Applications
· Research and the Future of Machine Learning
· Supporting Technologies

“We are delighted to host this event for the industry,” said Dr. Tom Smith, President and CEO of Geophysical Insights. “The Society of Exploration Geophysicists (SEG), the European Association of Geoscientists and Engineers (EAGE), and other sponsors are working with us to advance the knowledge of these nascent technologies that are beginning to transform our industry.”

Registration for the 2019 Oil and Gas Machine Learning Symposium is available at

Contact Information:
Oil & Gas Machine Learning Symposium
Laura Cuttill
Contact via Email

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