Digital Innovations in Oil and Gas with Geoffrey Cann

Oil and gas companies are sitting on vast amounts of surplus inventory, often without realizing its true scale or value. Equipment purchased for major projects frequently ends up idle, and stored across warehouses and laydown yards, while tying up capital and incurring ongoing costs. While some of this surplus results from project changes or cancellations, much of it reflects a deeper systemic issue around how industrial assets are tracked and managed. The challenge is greater than just the existence of surplus, and is much more about the inability to act on it. Data quality deteriorates as assets move through their lifecycle, internal systems fail to communicate across business units, and there are few effective tools to support reuse or divestment. At the same time, procurement incentives often favour new purchases over internal utilization. The result is a fragmented environment where companies unknowingly duplicate spend while valuable inventory sits unused. In this episode, I am speaking with Taylor Assaly, founder of IronHub, about how AI-driven data enrichment is transforming surplus management. He explains how structuring and scaling industrial data allows companies to identify, trust, and reuse existing assets—unlocking significant cost savings and improving capital efficiency. We also explore the broader opportunity to create internal and peer-to-peer marketplaces that increase asset utilization and turn surplus into measurable financial returns. 👤 About The Guest Taylor Assaly is the Founder and President of IronHub, a company focused on transforming how industrial organizations manage and monetize surplus inventory. With a background that spans entrepreneurship and frontline oilfield experience, Taylor has built IronHub into a platform that leverages AI to structure and enrich industrial data at scale. His work centers on improving asset visibility, enabling reuse, and driving capital efficiency across the energy sector. 💼 LinkedIn https://www.linkedin.com/in/taylor-assaly/ 🌐 Company Website https://www.ironhub.com/ ⚒️ Additional Tools & Resources 🎬 Go backstage and check out my studio: https://geoffreycann.com/mystudio/ 🎓 Take my one day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185 🔗 Connect with Me 🖥️ Blog series: https://digitaloilgas.substack.com/ 🎧 Podcast: https://geoffreycann.com/broadcast/ 💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/ 🤬 X (formerly Twitter): https://x.com/geoffreycann ⚠️ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.

EHS and AI are starting to transform safety and reliability in oil and gas. But most organizations are still managing safety as a compliance exercise, not an operational advantage. That approach is under pressure. Operations are more complex, regulations keep shifting, and critical EHS data is scattered across systems, spreadsheets, and sites. When something goes wrong, the impact isn't just regulatory — it shows up as downtime, lost production, and real harm to people and communities. There is a better way to run this. When EHS systems are connected and AI is applied to real operational data, safety becomes part of how the business performs day to day. Instead of reacting after the fact, companies can see risk building in real time, understand how conditions interact, and intervene earlier. The outcome is stronger reliability, fewer disruptions, and better decisions in the field. In this episode, I'm speaking with Amanda Smith, EVP Strategy at Cority. We discuss how EHS is shifting from compliance to performance, why most systems remain fragmented, and how AI is enabling real-time insight into operational risk. Amanda also shares the personal story behind her focus on safety, and a practical framework for how organizations can start using AI without taking on unnecessary risk. 👤 About The Guest Amanda Smith has spent more than 20 years working at the intersection of technology, people, and purpose. With a background in Industrial & Operations Engineering from the University of Michigan, she has built her career around helping organizations use technology to create healthier, safer, and more sustainable operations.Over two decades, Amanda has held leadership roles across product, strategy, and customer-focused initiatives, contributing to the evolution of modern EHS and sustainability software. Her work has centered on understanding how people actually use technology, how organizations create value through better decisions, and how connected data and thoughtful design can improve outcomes for workers and communities. Today, as Executive Vice President of Strategy at Cority, she draws on this experience to champion a strategy that stays grounded in what customers need most: solutions that are practical, scalable, and built to create lasting value. 🌐 Cority: https://www.linkedin.com/company/cority/ 💼 Amanda: https://www.linkedin.com/in/amanda-smith-432037a/ ⚒️ Additional Tools & Resources 🎬 Go backstage and check out my studio: https://geoffreycann.com/mystudio/ 🎓 Take my one day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185 🔗 Connect with Me 🖥️ Blog series: https://digitaloilgas.substack.com/ 🎧 Podcast: https://geoffreycann.com/broadcast/ 💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/ 🤬 X (formerly Twitter): https://x.com/geoffreycann ⚠️ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.

Digital twin technology is evolving rapidly, and when combined with artificial intelligence, it is starting to reshape how oil and gas operations are run in real time. For years, companies have relied on simulation tools to design and test assets, but these tools are slow and limited to planning use. In this episode, we explore how new approaches are closing the gap between simulation and real-world operations, enabling faster and more accurate decisions. I speak with Greg Fallon, CEO of Geminus AI, about how combining physics-based models with machine learning creates a new decision layer for industrial systems. This allows operators to simulate thousands of scenarios in seconds and improve production without new capital investment. We also discuss real-world applications in refineries, oilfields, and LNG facilities, where companies are seeing measurable gains in efficiency, output, and reliability. The conversation explores the shift toward autonomous operations, where AI supports or even makes decisions in complex industrial environments. Looking ahead, this technology opens the door to system-wide optimization, connecting assets across the value chain and helping companies operate closer to their true potential. #oilandgas #digitaltwin #artificialintelligence 👤 About The Guest Greg Fallon is the CEO of Geminus AI, a company focused on advancing digital twin technology through the integration of physics-based simulation and artificial intelligence. Greg is a mechanical engineer by training and spent much of his early career at Ansys, where he worked on advanced simulation technologies for industrial applications. He later led the global manufacturing product portfolio at Autodesk, expanding his experience across multiple industries. At Geminus, Greg is focused on enabling real-time operational decision-making by embedding AI into simulation models, helping industrial companies improve performance, efficiency, and reliability across complex systems. 💼 Greg on LInkedIn: https://www.linkedin.com/in/greg-fallon/ 🌐 Geminus: https://www.geminus.ai ⚒️ Additional Tools & Resources 🎬 Go backstage and check out my studio: https://geoffreycann.com/mystudio/ 🎓 Take my one day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185 🔗 Connect with Me 🖥️ Blog series: https://digitaloilgas.substack.com/ 🎧 Podcast: https://geoffreycann.com/broadcast/ 💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/ 🤬 X (formerly Twitter): https://x.com/geoffreycann ⚠️ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.

Global energy markets are under acute pressure as geopolitical disruption tightens supply and drives volatility. Events like the conflict in the Middle East are no longer regional—they ripple through fuel supply chains everywhere. Companies are not short of data. They are overwhelmed by it. Yet despite this abundance, assembling a clear operational picture still takes hours, leaving decision-makers reacting to a fast-moving market with a slow, fragmented view. The real constraint is not the decision itself—it is the time to assemble data and the even longer delay to execute. Information remains scattered across systems, emails, and legacy processes, often with high latency and inconsistent formats. At the same time, the industry operates as a network of hundreds of micro-markets—each terminal, tank, and junction representing a settlement point with its own dynamics. This fragmentation creates friction across the entire value chain, from inventory visibility to logistics coordination, limiting the ability to act on fleeting opportunities and, in some cases, preventing decisions from being made at all. In this episode, I speak with Ken Evans of DTN about how companies can compress decision cycles without increasing risk. We explore why data integration and workflow design matter more than raw analytics, how improving connectivity across the value chain can unlock margin, and where AI can play a practical role without taking control of decisions. Ken shares real-world examples of missed opportunities, execution bottlenecks, and emerging solutions that are helping companies move from fragmented, manual processes to faster, more resilient operations. 👤 About The Guest Ken Evans is a senior leader at DTN, a company specializing in data, analytics, and operational platforms for energy and commodity markets. He works closely with fuel suppliers, distributors, and terminal operators across North America to improve decision-making, supply chain visibility, and operational efficiency. Ken brings deep experience in connecting fragmented systems and data sources across the fuel value chain, helping organizations respond more effectively to market volatility and operational complexity. 💼. Ken on LinkedIn: https://www.linkedin.com/in/kendevans/ 🌐 DTN: https://www.dtn.com ⚒️ Additional Tools & Resources 🎬 Go backstage and check out my studio: https://geoffreycann.com/mystudio/ 🎓 Take my one day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185 🔗 Connect with Me 🖥️ Blog series: https://digitaloilgas.substack.com/ 🎧 Podcast: https://geoffreycann.com/broadcast/ 💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/ 🤬 X (formerly Twitter): https://x.com/geoffreycann ⚠️ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.

Turnarounds are one of the most critical and expensive activities in oil and gas, yet many are still managed using spreadsheets, manual coordination, and tribal knowledge. Despite years of investment in digital systems, there remains a persistent gap between turnaround planning and execution in the field. This gap creates real consequences. Turnarounds often run into the hundreds of millions of dollars, and delays of even a single day can result in millions in lost production. At the same time, safety compliance is frequently based on self-declared reporting rather than verified outcomes, and frontline workers are expected to operate with tools that were never designed for how turnaround work actually happens. The question is how to connect turnaround planning directly to execution without adding complexity. Advances in artificial intelligence, mobile technology, and workflow design are enabling organizations to automate large portions of turnaround planning, connect back-office systems with field execution, and validate work using real data captured on site. In this episode, I speak with Pankaj Pawan, CEO and founder of Maximl, about how his company is reinventing turnarounds with AI. We discuss how to reduce delays, improve safety, and bridge the gap between planning and execution by building systems that actually work for frontline teams. 👤 About the Guest Pankaj Pawan is the CEO and founder of Maximl, a company focused on improving industrial operations through artificial intelligence and workflow optimization. He began his career at Goldman Sachs working in quantitative analysis and computer science before moving into industrial technology. After visiting a refinery early in his career, he identified a major gap between modern digital tools and frontline industrial work, which led him to build solutions that connect turnaround planning with execution in the field. #turnarounds #oilandgas #AI ⚒ Additional Tools & Resources 📚 My books: https://geoffreycann.com/book/ 🎬 Go backstage and check out my studio: https://geoffreycann.com/mystudio/ 🔗 Connect with Me 📘 Industry Insight series: https://digitaloilgas.substack.com/ 🎧 Audio Podcast: https://geoffreycann.com/podcast/ 💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/ 👉 https://geoffreycann.com/contact/ ⚠ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.