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Machine Learning in Oil & Gas 2021
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Presentations
Wednesday, April 14, 2021
7:45 AM - 9:00 AM
Registration
9:00 AM - 9:10 AM
Chairman’s welcome
David Lafferty - Scientific Technical Services
9:00 AM - 5:00 PM
Live Streaming for Day 1
9:10 AM - 9:40 AM
Keynote presentation: the current status of machine learning in oil and gas: current trends, innovations and future opportunities
Deanna Zhang - Tudor, Pickering, Holt & Co
9:40 AM - 10:20 AM
Tech focus: an overview of the latest, transformational machine learning technologies to drive efficiency and productivity
Dr.Robello Samuel - Halliburton
Dr. Mohamed Sidahmed - Shell
Kevin Mackie - Siemens Energy
Daniel Viassolo - Schlumberger
Sylvain Calisti - Kayrros
10:20 AM - 10:40 AM
Morning coffee
10:40 AM - 11:20 AM
Panel discussion: machine learning case studies and practical insight in the brave new world of Covid-19
David Crawley - University of Houston
Catalina Herrera - TIBCO
Kalyan Venugopal - Schlumberger
Philip Black - Wood
11:20 AM - 11:50 AM
Measuring Data Performance in the Digital Oilfield
Curley Thomas - Chevron
11:50 AM - 12:20 PM
Digital Enablement - The Key to Success to Drive Your Digital Transformation
Graeme Gordon - Hess
12:20 PM - 1:20 PM
Lunch and networking
1:20 PM - 1:50 PM
21st Century Evolution of AI for Energy Production
Steve Roemerman - Lone Star Analysis
1:50 PM - 2:30 PM
Panel discussion: How AI is transforming the Oil & Gas industry
Konrad Konarski - AI Innovation Consortium
Adam Singer - ExxonMobil
Shashank Tomar - Tex-isle Supply Inc
2:30 PM - 3:00 PM
Lead AI-Powered Autonomous Oil and Gas Manufacturing with Deep Learning Optimization
Dr. Roberto Linares - Petuum Inc.
3:00 PM - 3:20 PM
Afternoon tea
3:20 PM - 3:50 PM
From Bots to Business: Using AI to solve real-world problems at scale
Jonathan Arneault - FuelTrust
3:50 PM - 4:20 PM
Accessing legacy data silos across firewalls and validating it for analytical purposes
Philippe Flichy - Keross
4:20 PM - 4:30 PM
Close of Day 1
David Lafferty - Scientific Technical Services
Thursday, April 15, 2021
8:00 AM - 8:30 AM
Revolutionizing asset performance and reliability with machine learning
Julian Zec - Maersk Drilling
8:35 AM - 8:50 AM
Aligning people, process and technology: achieving alignment between machine learning and skills transformation
Michael Yoho - CNX Resources
9:00 AM - 9:40 AM
Panel discussion: Covid-19 : the new normal for oil and gas and the implications on machine learning
Behrooz K-Hosseini - Saskatchewan Research Council
Dave Shook - Uptake Technologies
Cetin Karakus - bp
9:45 AM - 10:15 AM
The State of the Edge: The Impact of Edge-based AI in Oil & Gas
Sastry Malladi - FogHorn
10:15 AM - 10:45 AM
Break
10:45 AM - 11:15 AM
Playing the Data Translator role, "in advance", to encourage adopting Artificial Intelligence for adding value across an Oil & Gas E&P Asset
Juan Esteban Barreiro Gomez - Repsol
11:20 AM - 11:50 AM
The Science of Where – Unlocking Data’s Full Potential
Mansour Raad - Esri
Brian Boulmay - Esri
11:55 AM - 12:25 PM
BHP Atlantis project: Geoteric AI Fault Detection: A Subsalt Example
Hugo Garcia - Geoteric
Kelly Wrobel - BHP
12:25 PM - 1:00 PM
Lunch
1:00 PM - 1:30 PM
Finance Digital Transformation– Delivering Business Results
Monika Mangla - Chevron
1:35 PM - 2:05 PM
Leveraging Machine Learning to Thrive in the New Oil & Gas Reality
John Sturdivant - DataRobot
2:10 PM - 2:40 PM
Applied Machine Learning: Automation & Optimization
Fakhri Landolsi, Ph.D. - Equinor
2:40 PM - 3:00 PM
Break
3:00 PM - 3:30 PM
Machine Learning Demystified: when to invest in your own AI solution in Oil & Gas
Iurii Milovanov - SoftServe
3:35 PM - 4:05 PM
Forecasting Offshore Platform Integrity: Applying Machine Learning Algorithms to Quantify Lifespan and Mitigate Risk
Alec Dyer - National Energy Technology Laboratory/Leidos Research Support Team
Lucy Romeo - National Energy Technology Laboratory/Leidos Research Support Team
4:10 PM - 4:40 PM
Leveraging proactive predictive maintenance strategies to improve asset reliability and precision whilst minimizing downtime
Keith Holdaway - SAS Institute
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