HIGHLIGHTS FROM 2020
Thank you for a stellar, completely digital 2020 edition!
2020 Oil and Gas Machine Learning Symposium Wins Gold Hermes Creative Award for Live or Virtual Event
In the midst of unprecedented times and a global pandemic, 2020 was the year of creative solutions to new challenges.
The all-virtual 2020 Machine Learning Symposium was well-attended and featured thought leaders from E&P companies and tech firms across the globe. The virtual event space was the first of its kind in the Oil & Gas industry. In recognition of the quality, engaging format, and uniqueness of the virtual event space, the 2020 Oil & Gas Machine Learning Symposium won Hermes Creative Awards Gold in the Live or Virtual Events category. Check out the outstanding speaker lineup from 2020 as a gauge of what to expect from this year’s symposium – now the Energy Machine Learning Symposium.
The Virtual Experience
With the growing travel restrictions and community health concerns, the 2020 event was an immersive, all-virtual experience. In this new format, attendees were able to:
- Explore the event in 3-D, attending presentations in the auditorium and breakout rooms
- Ask questions and chat with speakers and other attendees live in-session
- Drop by the networking lounge to meet other experts
- Visit virtual exhibits, chat or video conference with booth staff
- Collect white papers and videos in a swag bag for later reference
- Participate in sponsor’s raffles for great prizes
- Enjoy access to presentations for up to 30 days post-event
The 2020 Oil & Gas Machine Learning Symposium hosted thought-leaders from E&P companies, consulting firms, and large technology companies. With a focus on machine learning in the energy, oil and gas sectors, the Symposium highlighted developments in AI, Machine Learning, Deep Learning, Data Analytics, Cloud Computing, and the Industrial Internet of Things (IIoT).
Breakout Session 1
Exploration & Development – Application of Unsupervised Machine Learning for 3D Seismic, Pliocene Turbidities, Offshore Nile Delta
Reservoir Characterization – Understanding the Reservoir Quality and Heterogeneity by Using Unsupervised Machine Learning Methods Applied to 3D Seismic Data
Field Operations – Intelligent Approach for Gosp Oil Recovery Enhancement
Dr. Mustafa Al-Naser
R&D Manager – Yokogawa
CV coming soon | View Abstract
R&D – Future Directions – Gas Hydrates, Reefs, Channel Architecture, and Fizz Gas: SOM Applications in a Variety of Geologic Settings
Breakout Session 2
Exploration & Development – A Multi-Disciplinary Approach to Establish A Workflow for The Application of Machine Learning for Detailed Reservoir Description – Wisting Case Study
Reservoir Characterization – Statistical Calibration of SOM Results with Well Log Data (Case Study)
Field Operations – Automated, Unmanned Detection and Quantification of Methane Fugitive Emissions
R&D – Future Directions – TABIO: An Open-Source Toolkit for Detecting and Segmenting Tables from Unstructured Documents with Machine Learning
Breakout Session 3
Exploration & Development – A Tale of Two Reservoirs: How Machine Learning can Help Define “Sweet Spots” in Conventional and Unconventional Reservoirs
Reservoir Characterization – Calibrating SOM Results to Wells – Improving Stratigraphic Resolution in the Niobrara
Field Operations – Overcome Siloed Approach for Optimization to Improve End-to-End Production with a Prediction-Optimization Framework
R&D – Future Directions – Transfer Learning for Subsurface Fault and Salt Interpretation