• Reviewing real-world results from predictive maintenance, production optimization, and leak detection to demonstrate tangible improvements in uptime, output, and emissions.
• Comparing pilot project results with year-round operational data to highlight the real-world impact and sustainability of AI solutions.
• Analyzing why some projects stalled-due to poor data, IT/OT handoff issues, or model drift-to help attendees avoid common and costly pitfalls.
• Identifying scalable patterns like small on-site models and trusted data search to guide effective and expandable AI deployment across assets.

• How AI, physics‑based models, and advanced analytics are reshaping asset lifecycle management across the oil and gas value chain.
• The evolution of digital twins from real‑time visibility to predictive, advisory systems embedded in day‑to‑day engineering and operations.
• Why standardized digital twin foundations are critical to moving AI beyond pilots and into trusted, repeatable operational use.
• A look at how cognitive, autonomous digital twins may transform decision‑making, workforce capability, and capital discipline over the next 3–5 years.

• Discovering different types of agentic systems and identify which one meets your business needs
• Understanding how you can scale and govern these systems to extract the most value
• Learning best practices on how to implement agentic systems

• Sharing quantified operator results for predictive maintenance, production tuning, and leak detection to identify high-value, repeatable wins.
• Translating AI outcomes into business metrics like uptime and throughput to build compelling business cases and secure executive funding.
• Detailing the playbook for moving from a successful pilot to a multi-site rollout to accelerate deployment and maximize ROI.
• Highlighting common pitfalls in data, change management, and oversight to protect safety and ensure long-term user adoption.

Three rapid 10-minute talks to spark discovery. One problem, one approach, one result.

• Implementing RAG (Retrieval-Augmented Generation) with effective connectors, caching, and guardrails to ensure AI provides accurate, context-aware answers from enterprise data. - Gulshan Singh, Medtronic and University of Houston
• Structuring schemas and metadata to dramatically improve the quality and relevance of information retrieved by AI systems - John Green, Kinetik
• Operationalizing AI and Unlock Value from the Systems You Already Have - Jeff Monk, Cohere

How to value AI initiatives and why there is a challenge there. Adoption challenges and what needs to be true for AI to stick at scale.

• Closing the gap between detection and response by pairing AI monitoring with real-time execution at remote sites.
• Triggering the right action fast, from isolating faults to initiating safe shutdowns when thresholds are breached.
• Modernizing control without rip and replace by moving toward virtual PLCs and software-defined control logic

• Driving AI adoption by designing around people, not just models and tools.
• Addressing how AI will reshape roles, org charts, and performance expectations.
• Reducing fear and resistance by clearly explaining how jobs will change.
• Building leadership accountability for the human side of AI design.

• Spotting the main ways enterprises are using AI assistants across teams and workflows, to prioritise the patterns most likely to stick.
• Pinpointing where organisations are getting the most value, to focus investment on repeatable outcomes not novelty.
• Avoiding the rollout pitfalls that stall impact, to protect trust, compliance, and adoption at scale.
• Applying practical guardrails for governance, quality, and security, to move from pilots to standard practice faster.

A leadership dialogue on embedding AI into the operating fabric—not just deploying algorithms·

• Redesign workflows, not just deploy models — Integrate AI into daily planning, execution, and control loops so it drives real-time operational decisions.
• Clarify decision rights and accountability — Define who acts on AI outputs, who can override them, and how risk is managed.
• Evolve the operating model — Align roles, capabilities, governance, and human–AI teaming across assets and remote operations centers.
• Embed AI into management systems and operating cadence — Integrate AI outputs into shift handovers, morning calls, production reviews, maintenance planning, and barrier management routines.
• Drive sustained adoption and trust — Focus on change leadership, explainability, frontline engagement, and measurable operational outcomes to scale beyond pilots.

• Security teams in remote operations make critical decisions with incomplete information, conflicting local reports, and verification delays that can stretch from hours to days.
• Discover how AI fused radio monitoring, sparse social media, and movement patterns in Chad into actionable forecasts, reducing decision latency while field teams maintained operational schedules.
• Learn what the model missed, when field operators overrode AI forecasts based on ground truth, and how the team calibrated trust between algorithmic predictions and local knowledge.
• Understand deployment in Chad, an austere technical environment: intermittent power and bandwidth, cloud access limitations, and strategies for handling potential data manipulation.
• See how to make actionable a proven approach that applies to emerging basins, offshore logistics, and other operations where information scarcity creates risk but operational continuity is essential.

Quantum Tech, celebrating its 100 year anniversary in 2025, is the promise of new computational capabilities, more powerful AI and transformative use cases for the energy sector. In this fireside chat some of the industry pioneers building these technologies will reveal the curtain on the truth - is quantum tech ready? How can you use it? What are the real applications in the energy sector? And why you must get started now even if there is no imminent ROI.

Join some of the most recognized founders and scientists from the quantum space for this engaging conversation that will leave you spooked and excited, with practical tips on how and where to get started with quantum now without large commitments or investment.

· Evaluating the trade-offs between large language models (LLMs) and smaller, domain-specific models based on cost, latency, and accuracy requirements.

· Selecting the right model architecture for the specific task from generative text applications to real-time sensor analytics to optimize performance and value.

· Balancing the flexibility of general-purpose LLMs with the precision and efficiency of smaller models to meet the stringent demands of operational environments.

Rapid 10-minute talks to spark discovery. One problem, one approach, one result.

• Explore why traditional "Data Lakes" are dangerous for Agentic workflows and how shifting to a "Semantic Data Product" strategy creates the governed, context-rich environment required for AI at scale. - Catalina Herrera, Dataiku
• Selecting the right AI tool for each task based on a balance of cost, latency, and accuracy to optimize overall system performance and ROI. - Meenakshi Mishra, Principal Data Scientist, ExxonMobil
• How Chevron is approaching responsible AI in practice - Kelby Reding, Chevron
• · Session by Outsystems

• Standardizing asset data models across sites to build a scalable digital twin foundation on top of PI Asset Framework and the Covestro Monitoring Platform.
• Detecting early anomalies on heat exchangers, safety valves and other critical equipment using rule based and ML based monitoring to avoid unplanned outages and safety risks.
• Triggering targeted maintenance actions through automated alerts and intuitive dashboards so engineers can act before fouling, leaks or degradation impact production.
• Sustaining value by defining clear workflows for reviewing alerts, tuning models and reducing false positives so digital twins remain trusted tools for reliability teams

Get a rapid-fire look at what is next in industrial AI. In this high-energy showcase, six startups each have six minutes to pitch practical solutions built for oil and gas.

You will:

• See a curated showcase of deployable AI technologies and applications designed for real operational environments.
• Compare commercial models, integration requirements, and typical timelines from pilot to production.
• Hear real-world examples and measurable outcomes aligned with operator priorities.
• Vote live for the solution you would most realistically trial in your own organization.

Audience Choice Award
The winning startup receives a complimentary exhibition space at another Energy Conference Network conference.

For more information, please contact bryony.meredith@energyconferencenetwork.com

Facilitator: Sean Barnes, Wolf Executives

Innovators

• Building a strategic portfolio approach to AI investment to move from scattered pilots to coordinated, enterprise-wide transformation.
• Establishing a center of excellence and cross-functional steering groups to standardize best practices and accelerate organization-wide learning.
• Developing a vendor and technology strategy that balances build-vs-buy decisions to ensure scalability, interoperability, and long-term value.

• Clarifying why single-model strategies fall short in safety-critical, regulated operations, for reducing operational and compliance risk, to protect uptime and trust.
• Explaining how a flexible data integration layer enables model choice across cloud, on-prem, and hybrid environments, for avoiding vendor lock-in, to match the right model to each operational task.
• Demonstrating how to integrate geospatial, operational, and external data into an AI-ready foundation, for improving context and retrieval quality, to increase accuracy and adoption in real workflows.
• Introducing Model Context Protocol and the shift toward decoupling data from models, for adapting as models and regulations change, to add new capabilities without re-architecting the data layer.

• Defining risk-based decision criteria that determine which procurement and supply-chain processes are truly ready for AI and where human oversight must remain central.

• Aligning people, process, and technology to enable AI responsibly, with clear governance, measurable value realization, and built-in accountability.

• Establishing structured guardrails that balance automation efficiency with expert human judgment in high-impact sourcing, contracting, and supplier decisions.

• Why existing digital investments stall at dashboards and pilots
• Leveraging a full stack approach to unlock value
• What moves the needle: NPT reduction, lift cost, and well performance at scale

• Why Noble set up the AI Steering Committee and what problem it solves.

• How you manage collaboration across departments and reduce overlapping solutions.

• How you approach governance, risk assessment, and compliance in AI initiatives.

• What has worked well so far, and what you would do differently.

• Practical recommendations for other operators setting up a similar model. Featured operator: Noble Corporation

Patrick "PJ" Janes, Noble Corporation

Darren Shelton, Moran Shipping

• Turning existing temperature, pressure and subsurface data into actionable guidance with clear next steps for operations.
• Predicting compressor failures from sensor data to trigger proactive maintenance.
• Optimizing gas lift using pad and downhole-aware setpoints to maximize production.
• Mitigating frac hit risk using geospatial and pressure analytics with protective action guidance.

• The shifting line between human work and machine work
• Skills that matter most in the next 3 to 5 years
• Workforce readiness beyond training: culture, trust, and adoption
• How leaders can drive transformation without resistance
• Building resilience in a world of nonstop technological change

• Delivering Real-World AI Use Cases in Oil & Gas: Lessons from the Field
• UiPath: The GOAT for BOAT (Business Operations and Automation Technologies).
  Gavin Keeler, UiPath
• Generating daily AI summaries for well and asset performance triage to accelerate decision-making and prioritize intervention efforts.
• Using machine learning to identify schedule risks in turnaround planning, enabling proactive mitigation and protecting project timelines.

· Leveraging data platforms to break down subsurface and operations data silos and create a unified foundation for AI applications

· Ensuring AI models have access to trusted, well contextualized data with clear lineage to improve reliability and support stronger decision making

· Addressing practical implementation challenges, from data migration to taxonomy alignment, to enable scalable AI solutions across the asset lifecycl

Move from presentation to problem-solving in these highly interactive, peer-driven roundtables. Each roundtable covers a different topic, allowing attendees to choose the discussion most relevant to them.

Roundtable leaders will open with a brief 5-minute introduction and a short overview of the topic, which can include a case study, lessons learned, or an industry update. This is followed by a 30-minute peer discussion, with the leader facilitating questions, feedback, and practical exchange. The final 5 minutes are dedicated to aligning on five clear industry recommendations which will be included the post-conference report.

Choose one topic that aligns with your most urgent priority.

• Building Your AI Center of Excellence: Scope, Charter, and RACI - Jeff Monk, COHERE
• Proving AI's Bottom-Line Impact: From Unit Economics to Portfolio ROI - Michael Maltsev, RigER
• Fueling GenAI with OSDU: Taxonomies, Security, and Speed
• Governing Autonomous Agents: Approval Matrices, Audit Trails, and Rollback - Michael Bachman, Boomi
• Winning Hearts and Minds: Change Management for AI on the Frontlines - Mathias Klinkby, Noble Corp 
• The Data Defects That Derail AI: Prioritizing Quality for Trusted Answers - Jacob Dittenhauser, Syniti
• Your AI Vendor Blueprint: Build, Buy, or Bolt-On for 2026-2028 - David Crawley, University of Houston
• LLMs vs. Small Models: Matching the Tool to the Operational Task - Olu Obafemi, CGI
• Designing the Human-in-the-Loop: Rules for Seamless AI-to-Human Handoffs - Nathan Dube, NTT DATA
• AI at the Edge: Solving Power, Connectivity, and Harsh Environment Challenges - Jonathan Doan, ZEDEDA
• The AI-Powered M&A Playbook: Rapid Data Integration in the First 12 Weeks
• The AI Purchasing playbook – The Perspective makes all the difference - Peter Dill
• Leveraging AI to Lead at Scale - Sean Barnes, Wolf Executives
• Taming the 'LLM Mess': Orchestrating a Secure, Multi-Model Strategy for Oil & Gas - Catalina Herrera, Dataiku
• The Data Defects That Derail AI: Prioritizing Quality for Trusted Answers - Session by Syniti
• Operationalizing Agentic AI across critical business processes in oil and gas. - Gavin Keeler, UiPath
• Latest Trends in GenAI for Material Discovery: Separating Hype from Real Value Drivers - Scott Healey, SandboxAQ

• Business case that clearly articulates the problem and the proposed solution

• Compelling financial metrics that resonate with decision-makers

• Clear risk mitigation and value creation metrics

• Navigating the customer's budget approval process

• Enabling champions to become effective advocates

• Deploying AI models capable of operating reliably in harsh environments with limited power, connectivity, and computing resources.
• Managing model drift at the edge by establishing triggers for retraining and developing efficient data syncing strategies with the cloud.
• Ensuring the robustness and failure-resilience of edge AI systems to maintain operational integrity and data continuity in remote locations.

• Explaining why Oxy is accelerating in-house application development to reduce cost and dependency on external vendor innovation.
• Showing how AI tools are compressing the SDLC from idea to prototype, for faster iteration and clearer business alignment.
• Demonstrating how AI is supporting development workflows, including code quality checks and automated testing, to improve speed and reliability.
• Sharing practical lessons on where AI adds the most value across the SDLC, for scalable adoption across teams.

• Transforming Catalysis R&D: Moving from “Explore and Exploit” to Systematic In-Silico Screening with Large Quantitative Models - Scott Healey, SandboxAQ
• Curating metadata and taxonomies that are purpose-built for operations to dramatically improve the relevance and accuracy of AI-generated answers.
• Monitoring data-quality KPIs that are explicitly tied to financial outcomes to focus data management efforts on what truly impacts the bottom line. - Isaac Meisner, Syniti

This session is dedicated to elevating the voice of industry leaders driving real-world AI adoption. It is designed as an open forum to surface concerns, priorities, and execution barriers across operational AI deployment.

Insights captured will directly inform the AI Applied Consortium’s AI Policy Posture, which has contributed to Federal AI engagement and White House level policy discussions. Through the Consortium’s Policy to Practice initiative, this forum ensures real industry realities are translated into the national AI conversation.

The objective is simple: bring operational truth to the policy table.

Discussion Focus:

• What is slowing AI adoption beyond pilots
• Governance, trust, security, and workforce-readiness
• Infrastructure and data constraints
• Where industry needs clearer federal alignment and support

Led by: Konrad Konarski, Ai Applied Consortium

Collaboration between SLB Production Chemical Technologies and the University of Houston Cullen College of Engineering Applied Data Science Lab (Director Dr. Renie Hu) introduces a data-driven framework that transforms raw assay data into operational intelligence, enhancing treatment design, consistency, and scalability in oilfield chemical management. Results:

• Enables faster decision confidence and enhanced performance to improve reservoir throughput
• Architects massive deposit data analysis with predictive patterns of productivity
• Fine-tunes supply chain efficiencies to mitigate risk and support wellhead lifetime value across reservoir geographies
• Automates major parts of deposit-treatment workflow to compress time to action

Led by: Regan Jones, PhD, Senior Staff Scientist, SLB 

David Crawley, UH College of Engineering