Machine learning has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In the low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized from using machine learning in oil and gas operations.
But 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 2nd 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.
- What you can expect from case studies, panel discussions, and roundtable sessions:
- Understanding the needs of your business and identifying viable use cases for machine learning
- Creating a technology roadmap for the short- and long-term in line with business objectives
- A/B testing and implementing machine learning solutions effectively (including interoperability with other systems)
- Examples of successful outcomes from machine learning in oil and gas operations (including an emphasis on tying ROI back to business objectives)
For more information about this event, please visit: energyconferencenetwork.com