This presentation discusses specific cases of how AI, both generative AI with Large Language Models, and Machine Learning have been used to detect and eliminate methane and other greenhouse gas emissions. The first example involves evaluating a service company's software package that uses historical and current measurements of oil and gas operations to analyze and forecast emissions. We will examine Chevron's blueprint for reducing methane emissions in the Permian Basin and discuss areas where Machine Learning and Deep Learning have been used, and where generative AI is being used for repurposing data sets for future needs.

Dr Susan Nash

Director of Innovation & Emerging Science and Technology
AAPG

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Learn how Entergy is investing in Artificial Intelligence at scale and the lessons it has learned in the rapid development and expansion of their AI function. In this insightful case study Energy’s Chief AI Officer Andy Quick will share details on the journey including:

● Leveraging AI with advanced metering infrastructure to predict when distribution transformers are likely to fail ● Enabling proactive maintenance that prevents unplanned outages

● Building out and defining the AI function, beginning in 2023, and how AI is positioned internally today

● Developing an enterprise AI strategy that balances value, capability, and risk

● Determining which of the use cases across the value chain to pursue - and which not to

● Approaching the conundrum of centralised or decentralised model, CoE or federated, in-house or outsourcing

Andy Quick

Chief AI Officer
Entergy

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● Discovering how real-time data analytics can optimize decision-making processes, leading to increased operational efficiency and reduced downtime ● Learn how leveraging AI and ML in real-time data streams can improve yield by identifying and mitigating inefficiencies across production lines

● Accelerating business outcomes, delivering faster insights and enabling proactive responses to operational challenges

● Understanding the infrastructure and tools required to harness real-time data for continuous improvement

● Using predictive AI to reduce unplanned downtime by up to 50%, boost operational efficiency and minimize revenue loss ● How predictive analytics can extend asset life by 30%, significantly lowering replacement and repair costs over time

● Discussing the impact of predictive maintenance to allocate resources more effectively

● Detecting and preventing potential failures before they occur, supporting safer operations and regulatory compliance

When approaching an AI project, there are many paths that can be taking to get to the desire endpoint. Vendors are innovating and creating valuable solutions but understanding who and which solution to use can be a real headache. Moreover,

novel use cases on unique assets may require novel, self-built, solutions. Our panellists will help you through this process, discussing:

● Balancing cost with control: Buy to cut development time by 40%? Build for increased control over customization and long-term cost savings? ● Dissecting the impacts on speed to operationalization

● Do talent gaps in the face of AI-specific developer shortages make consultants and vendors a necessity?

● How to strike the hybrid sweet spot

Mark Bohn

Vice President, Transformation and Enterprise Portfolio Management
Western Midstream

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Jason Gee

Data Science Manager
ExxonMobil

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Paolo Morra

Director of Analytics
DTE Energy

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● Taking inspiration from AI-based game engines to solve real-world supply chain challenges

● Shifting your supply chain strategy from reactive to predictive and preemptive, overcoming disruption

● Leveraging new technologies and effective leadership to scale your supply chain models for enterprise success

● How Chevron Phillips Chemical Company is using deep reinforcement learning, leading to breakthroughs in their supply chain strategy

Jacob Herzog

Data Scientist
Chevron Phillips Chemical Company

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As the energy sector increasingly embraces AI, the pressure to ensure long-term ROI will grow. Gartner estimates over 50% of AI initiatives fail to deliver sustained value, primarily due to poor scalability, governance issues, and misalignment with evolving business needs. In this session our panellists will discuss strategies you can deploy today to ensure you future-proof your investments for tomorrow. Key topics include: 

• Building AI systems that can scale across operations and adapt to new data, use cases, and emerging technologies without constant rework 
• Implementing governance frameworks that mitigate compliance and ethics risk in a rapidly changing regulatory landscape 
• Ensuring collaboration between data scientists, engineers, and operations teams to maintain AI models, adjust them to operational changes, and sustain value 
• Embedding a culture of AI-centricity, data-centricity, and of continuous learning and development  

Erin Boyd

Chief Digital Commercial Transformation Officer
AES Corporation

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Jennifer Schwertz-Ohrin

Chief Innovation Officer
G.I.S

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75% Interactive for Maximum Learning

Take a break from the PowerPoints with this interactive session and further your conference takeaways by actively participating and learning realistic ways to adapt your AI strategy long after the summit ends. This Masterclass will help you map your vision – reflecting on what you’ve heard over the past two days and work on key takeaways you can communicate to your executive team tomorrow. The opportunity to learn from your peers will provide critical and insightful industry perspectives.

Building and Sustaining a Centralized AI Capability

Operators are increasingly looking to leverage AI across their operations by developing out a centralized AI capability. Perhaps the summit has inspired you to start this process, or perhaps you’re looking to develop your current organizational capability. This masterclass will help you navigate the challenges and various paths which can be taken in not only building out a centralized AI capability but building out a capability which is sustainable and consistently delivers value to your organization.

The Masterclass will focus on:

● Opportunities and challenges with adopting a centralized functional model for AI

● Hype vs reality in AI and positioning a data science team for long-term success

● Approaches on deciding whether to re-use, rent, build or buy an AI solution

● Experiences in development & change management for predictive AI models that improve performance in commercial & supply chain operations

● Developing credibility of recommendations and sustaining long-term performance of AI models

Jason Gee

Data Science Manager
ExxonMobil

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