View the schedule for Day one of the AI in Energy Summit, Houston's leading energy conference and learn how to augment your workforce, maximize asset performance and power intelligent operations
In this session, we explore the challenges and opportunities of applying AI in a multi-operator environment, where data from thousands of operators must be securely separated, yet intelligently connected. Raymond will share how his team is building a cloud-based data lake and partnership ecosystem to support scalable, secure, and actionable insights across regions like the Permian.
As AI matures from experimentation to enterprise-wide deployment, leaders are under pressure to define strategies that will deliver measurable value. This keynote panel explores what a future-ready AI strategy looks like and what leadership must do now, to get there.
In the race to unlock AI’s full potential, data is both the fuel and the friction. As companies battle to integrate diverse control systems, legacy platforms, and siloed datasets, the question isn’t just how to centralize data, but how to make it usable, trustworthy, and AI-ready? With Generative AI offering new ways to structure and interpret messy data, is it time to rethink the traditional “clean first, deploy later” mindset? This session challenges conventional data strategies and explores how AI can reshape, not just rely on, your data foundations.
In energy operations, critical knowledge is often buried in unstructured text, maintenance logs, field reports, technical manuals, and supplier documents. In this session, we will demonstrate how Natural Language Processing (NLP) can unlock that information and turn it into real-time operational insight. Discover how frontline teams are using NLP to reduce downtime, improve compliance, and make faster, more informed decisions, without needing to sift through thousands of documents.
As digital transformation accelerates, the ability to scale AI across operations hinges on a solid data foundation with modernized data architecture. Yet many still struggle to move beyond siloed analytics and pilot projects due to fragmented systems, unclear ownership, and inconsistent data quality. This session explores how to identify high-impact pain points and design data architectures that support scalable, secure, and AI-ready analytics environments.
As energy systems grow more complex and data-rich, the next frontier is not just prediction, but action. Agentic AI introduces autonomous, goal-driven agents capable of making decisions, coordinating tasks, and adapting to changing conditions in real time. This panel brings together operators who are exploring or deploying agentic AI to streamline operations, reduce manual intervention, and accelerate response times across asset-heavy environments.
As operations grow in complexity, the next frontier lies in orchestrating autonomous decision-making across entire business units. This session explores the theoretical and emerging practical applications of hierarchical multi-agent systems, a layered architecture of AI agents that collaborate, communicate, and self-organize to optimize workflows across drilling, subsurface, completions, and abandonment.
As the industry seeks to move beyond reactive maintenance, machine learning offers a powerful opportunity to predict failures before they happen, reducing downtime, optimizing asset performance, and improving safety. This panel explores how early-stage pilots can lay the foundation for scalable, enterprise-wide predictive maintenance solutions.
Monitoring remote and offshore oil fields is a critical challenge. By replacing manual inspections and sensor-heavy setups with real-time video analytics, site surveillance can be digitized, improving uptime, reducing maintenance delays, and increasing productivity. The result: scalable, cost-effective field visibility without the need for constant human presence or complex sensor networks.
As utilities modernize their infrastructure, the ability to extract actionable insights from visual and operational data is becoming a game-changer. In this session, Eversource Energy, New England’s largest utility, shares how they are deploying computer vision and machine learning to assist asset inspection, predictive maintenance, and internal engineering workflows for transmission and distribution systems.
In today’s volatile landscape, traditional workforce planning is no longer enough. Lisa shares how Dow is rethinking talent strategy from within operations, not HR, by identifying the urgent need for AI-powered models that can forecast, modernize, and redeploy talent across global sites. This session will explore how to build a resilient, data-informed talent strategy that meets the demands of a rapidly evolving marketplace.
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As operators face increasing pressure to improve safety and reduce downtime, AI-powered leak detection is emerging as a high-impact solution. This use case will illustrate how computer vision, thermal imaging, and edge computing are being combined to monitor pipelines in real time, enabling early identification of leaks without relying solely on traditional sensors.
Timely, data-driven decisions are essential for improving reliability, reducing costs, and managing operational risk. This session focuses on how AI and advanced analytics are transforming asset management by enabling predictive maintenance and risk-based decision-making. Learn how to:
As wells reach the end of their productive life, maximizing output becomes increasingly complex, especially for artificial lift systems like plunger lift and gas-assisted plunger wells. This session explores how machine learning is being used to optimize production by identifying the ideal shut in and flow times, gas injection rates, and equipment configurations. After a successful pilot, Ovintiv is moving toward full automation, allowing models to recommend optimal settings and prioritize wells for intervention.
In energy operations, visual data is everywhere, but often underutilized. Computer vision platforms powered by AI are changing that, enabling automated inspections, real-time risk detection and intelligent monitoring of assets. In this session, we’ll explore how computer vision is being used to enhance maintenance programs, improve the safety for frontline workers and deliver continuous visibility into operational environments. Discover how energy companies are leveraging these solutions to reduce manual effort, detect anomalies before they escalate, and drive smarter, safer decisions across the enterprise.
As experienced engineers retire and operational demands grow, refineries are turning to AI to bridge the gap. In this session, Tim shares how anomaly detection is being used to surface early signs of equipment issues, enabling proactive maintenance, reducing unplanned downtime and supporting a shift towards predictive maintenance.
Using a dynamic risk analyzer, the team used a hands-off AI tool that automates data cleansing, modeling, and real-time monitoring. It flags subtle shifts in process behavior, such as changes in reactor temperature slopes, that may signal emerging equipment issues. By surfacing these early indicators, engineers can plan interventions more effectively and reduce the risk of costly failures.
The session also explores how to simplify model building and analysis, enabling engineers to extract insights from thousands of data points without writing code. While the pilot has shown promising results, human oversight remains critical, with a “trust but verify” approach guiding the path toward enterprise-wide adoption.
As regulatory frameworks evolve and voluntary reporting commitments expand, organizations face mounting pressure to streamline how they collect, validate, and report operational data. This session explores how AI-powered compliance and reporting systems can dramatically reduce manual effort, enhance data integrity, and empower frontline teams to focus on high-value tasks. By embedding intelligence into reporting workflows, organizations can unlock new levels of agility, transparency, and safety.
This session explores the use of Generative AI to automate and improve Root Cause Analysis (RCA) on customer quality complaint data. By combining domain-specific language models with structured and unstructured feedback records, Dow’s system uncovers hidden patterns, links operational issues to customer experiences, and generates coherent RCA summaries. The approach streamlines investigation workflows, enhances consistency in issue resolution, and supports proactive quality improvements.
Learn how AI voice-enabled assistants and smart wearables are empowering field workers with instant access to data, procedures, and remote support, boosting productivity while keeping hands and eyes on the job.
This session examines methods that combine artificial intelligence with other approaches to improve predictive diagnostics in industrial production, particularly in scenarios with limited data and uncertainty. The discussion includes examples of AI applications for predicting heat exchanger fouling. The case study demonstrates how integrating physics-based models with machine learning can enhance reliability, reduce downtime, and support more cost-effective decision-making in energy management.
Powerful simulation tools provide the opportunity to increase the reliability, resilience and productivity of your asset portfolio. By creating models of your assets your organization can model, predict and optimize performance. From understanding the likelihood of equipment failures to drive predictive maintenance, to understanding how your assets will behave in different operating conditions to drive performance optimization, forecasting the health of your asset is key to retaining safe and efficient operations.
When every function across ExxonMobil came knocking with the same request of “We need a smarter way to interact with our documents”, the data science team knew an Enterprise-scale generative AI powered solution was the answer. The vision was to create a solution that empowered employees to ‘speak with their documents’, instantly summarizing complex technical reports, contracts, and operational manuals into actionable insights. The potential to drive productivity enterprise wide and eliminate hours of manual effort was undeniable.
Data alone doesn’t drive optimal decision making, context does. As organizations integrate diverse data sets across business units, advanced analytics and visualization platforms are enabling a new level of operational clarity. In this session, we’ll explore how combining smart energy analytics with human intelligence unlocks deeper insights, improves forecasting accuracy, and supports smarter resource allocation. Discover how energy leaders are using intuitive dashboards and cross-functional data models to make faster, more reliable decisions, while maintaining the human judgment that keeps operations resilient and adaptive.
Looking to discover the latest AI technologies transforming energy operations? The AI Spotlight Session is your opportunity to get up close with the innovators shaping the industry. During this high-energy networking break, you'll rotate through vendor booths in rapid 5-minute intervals, giving you focused time to explore each solution, ask specific questions, and understand exactly how these technologies can address your challenges. Whether you're evaluating a new tool, seeking a strategic partner, or just want to stay ahead of the curve, this session is designed to deliver maximum value in minimal time. Connect directly with solution providers, gather actionable insights, and walk away with ideas you can take back to your team and start implementing right away.
Marathon Petroleum is laying the groundwork for AI powered technology with a transformational program designed to standardize maintenance operations across its midstream business. With a focus on data quality, system integration, and process consistency, this initiative is enabling scalable AI use cases across the full IPSEC lifecycle (Identify, Plan, Schedule, Execute, Close). As Dan shares, this foundational work is critical, especially in light of a recent MIT study showing that 95% of AI initiatives deliver little to no measurable value. This session explores how Marathon is building the digital and data infrastructure needed to ensure AI delivers real operational impact and how they’re actively exploring AI use cases that support predictive maintenance, intelligent scheduling, and frontline decision-making.
Computer vision technology is driving productivity and safety improvements across operations, helping to detect anomalies, identify safety issues and automate inspection processes. This session we will explore the key applications of computer vision and how to implement them.
As AES shifts from a global digital model to a hub-and-spoke structure, it’s reimagining how innovation scales across the enterprise. At the heart of this transformation is a new Digital Center of Excellence, led by Erin, who is tasked with building future-ready platforms that not only keep pace with AI’s rapid evolution but also empower teams to explore what’s possible. With a bold mandate to triple energy capacity without increasing headcount, AES is betting on AI to drive both cultural and operational transformation.
Where are you on your AI journey, and who else is walking the same path? Grab a drink and join this dynamic networking session to find and connect with peers at a similar stage of AI readiness. During sign in, attendees are requested to select a badge that identifies their AI readiness level from: