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|Wednesday, April 13, 2022|
|8:00 AM - 8:50 AM|
|8:50 AM - 8:55 AM|
|8:55 AM - 9:25 AM|
• Digitization and Digital Transformation: Trends for 2022
|9:25 AM - 10:10 AM|
Panel Discussion: The Road to Net-Zero: The Role Machine Learning Will Play in Facilitating Sustainable Operations
Technology evolution is driving the shift to cleaner energy. The wealth of data generated will provide insight into supply chain visibility, help understand emissions, and abate options better.
David Lafferty - Scientific Technical Services
Wafik Beydoun - IOGP
Andy Hock, PhD - Cerebras System
Brittney Marshall - BHP
Aria Abubakar - Schlumberger
|10:10 AM - 10:40 AM|
- Can predictive analysis reduce carbon emissions and help meet carbon reduction goals?
|10:40 AM - 11:10 AM|
|11:10 AM - 11:40 AM|
|11:40 AM - 12:10 PM|
|12:10 PM - 1:40 PM|
Including a welcome address from Sean Cahill.
|1:40 PM - 2:10 PM|
Major priorities with Machine Learning are preparing data, developing a model to train it, and then deploying the model, but what are the components of a successful Machine Learning implementation project?
|2:10 PM - 2:40 PM|
In recent years, the Oil and Gas sector made significant investments in its data analytics and Artificial Intelligence initiatives. However, recent studies show that these initiatives are stalling and have a low return on investment due to misalignment between business needs and the Artificial Intelligence solutions developed, data availability, access, and quality, slow adoption of resolutions by business segments, failure to scale and productionize, and decision makers’ inability to fully understand the value Artificial intelligence can add to their portfolios. This session highlights the success of BHP’s Artificial Intelligence initiatives within E and P and lessons learned from their journey.
|2:40 PM - 3:10 PM|
From Data Center Modeling to Edge Deployments with Monitoring: Domino Enterprise MLOps for Oil & Gas Machine Learning
The Oil & Gas industry presents unique challenges for developing and deploying valuable models. Heterogenous infrastructure at the edge, complex enterprise architectures, and organizational complexity mean it’s hard to drive a seamless, low-friction machine learning process. Learn how Domino’ end-to-end workflows (powered by Dell hardware) make Enterprise MLOps a reality.
|3:10 PM - 3:40 PM|
|3:40 PM - 4:10 PM|
Incredible risk and disruption is driving the need for companies to adapt and drive transformational and change efforts. However, the track record and return on these investments are horrible. Al will posit five specific reasons why these efforts fail with references to and examples from the topics being discussed at this conference (big data, machine learning, AI or analytics efforts) – with a goal for attendees to learn how to avoid these issues with the right approach.
|4:10 PM - 4:40 PM|
- Effects of complexity, principal-agent issues, and human cognitive biases on oil and gas megaproject outcomes
|4:40 PM - 5:00 PM|
- Blending compatible technologies for better risk analysis
|5:00 PM - 6:00 PM|
5-minute welcome from Andy Hock followed by a complimentary drinks reception and hors d'oeuvres.
|Thursday, April 14, 2022|
|8:00 AM - 8:50 AM|
|8:50 AM - 9:00 AM|
|9:00 AM - 9:30 AM|
|9:30 AM - 10:00 AM|
- Using deep learning to accelerate the process of seismic integration
|10:00 AM - 10:30 AM|
- Understanding the dynamic resource requirements of Artificial Intelligence/Machine Learning based workloads
|10:30 AM - 11:00 AM|
|11:00 AM - 11:15 AM|
- Using artificial intelligence to read P&IDs and Isometrics of industrial plants to build a digital model "twin" of the plant
|11:15 AM - 12:15 PM|
- How can incident report libraries be strengthened? What information shouldn’t be left out?
Konrad Konarski - AI Innovation Consortium
Candance Axel - TechnipFMC
David Crawley - University of Houston
Adam Berg - TechnipFMC
Elias Brown - Vallourec North America
|12:15 PM - 1:15 PM|
|1:15 PM - 2:00 PM|
- Shared examples of supply chain transformations highlighting advantages to bottom lines
|2:00 PM - 2:30 PM|
• Challenges facing the Artificial Intelligence and Machine Learning for improving oil and gas production
|2:30 PM - 3:00 PM|
- Unpacking Chevron’s data science history- the early years and beyond the Artificial Intelligence and Machine Learning hype in 2010
|3:00 PM - 3:30 PM|
|3:30 PM - 4:00 PM|
- Applications of Artificial Intelligence and Machine Learning for improving oil and gas production
|4:00 PM - 4:45 PM|
- How are business models affected by putting employees at the centre of technology changes?
Matthew Fry - CGG
Irina Prestwood - Chevron
Bilu Cherian - Premier Oilfield Group
Catalina Herrera - Dataiku
|4:45 PM - 4:50 PM|