2018 MACHINE LEARNING IN OIL & GAS CONFERENCE Event Listing - RiggerTalk

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2018 MACHINE LEARNING IN OIL & GAS CONFERENCE

April 18, 2018 to April 19, 2018 — Starts at 8:30 AM

April2018

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Contact

Location Houston, TX
US

Web + Email https://www.machinelearning-oilandgas.com/home

symon.rubens@energyconferencenetwork.com

Description

ABOUT THE EVENT

Improve efficiency, safety and reduce costs with machine learning in oil and gas Machine learning has now become an integral part of the operations of most oil and gas companies, allowing them to gather large volumes of information in real-time and translate data sets into actionable insights. They now need to view data as an extremely valuable resource, with huge upside for companies with innovative, robust machine learning strategies. In the ongoing ‘new norm’ low commodity price environment, saving time, reducing costs, boosting efficiencies and improving safety are all crucial outcomes that can be realized from using machine learning in oil and gas operations. Investing in predictive technology to become more productive is easier said than done. What are the strategic considerations for adopting an artificial intelligence (AI) platform? How do you implement an AI platform seamlessly with other systems and ensure there’s no cost blowout? And what’s the guarantee for return on investment (ROI)? The 3rd annual Machine Learning in Oil and Gas Conference will explore these challenges and more. Specialists from leading upstream, midstream, and downstream companies will come together to exchange insights on the latest developments in machine learning. Case studies and interactive panel discussions will enable delegates to also benchmark against industry best practice. In 2017 over 150 attendees benefited from attending the world’s largest and most respected gathering of oil and gas data experts – we hope you can join us in 2018. By 2020, the consulting group Gartner estimate that over 25 billion devices will be connected to the cloud, many of them new sensors deployed across multiple industrial domains.

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