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

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🎓 Take my one day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

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⚠️ 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/

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🖥️ Blog series: https://digitaloilgas.substack.com/

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⚠️ 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/

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💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

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⚠️ 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/

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🤬 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/

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⚠ Disclaimer The views expressed in this podcast are my own and do not constitute professional advice.

The global oil market still runs on a horribly fragmented system where physical barrels, paper contracts, and financial settlement operate in parallel but disconnected layers. Transactions are dependent on manual processes, emails, PDFs, and handshake relationships built over decades. For a trillion dollar industry, the core trading infrastructure remains slow, opaque, and largely inaccessible for all but the largest players.

This creates real operational and financial constraints. Settlement delays can stretch to 90 days, tying up working capital and exposing companies to liquidity risk. Market access is hobbled by lengthy know-your-customer processes and high barriers to entry.

Here in the spring of 2026, with the Strait of Hormuz blocked, these inefficiencies add up to systemic challenges that will make eventual recovery much harder than it needs to be.

It's time for a new approach, one that digitizes the physical barrel itself. By tokenizing crude oil into smaller, tradable units, markets can accelerate settlement, improve transparency, and broaden participation. Imagine a token that represents a real, physical quantity of oil, enabling traceability across the supply chain and creating a new layer of market visibility. This also opens the door to fractional ownership and new forms of capital access that were previously unavailable.

In this episode, I am speaking with Baron Lamarre, co-founder of INDEX, the International Digital Exchange, about how tokenization could fundamentally reshape oil trading. We explore the inefficiencies of today's system, the mechanics of turning barrels into digital assets, and why this shift could materially improve cash flow, transparency, and market access across the energy sector.

👤 About The Guest

Baron Lamarre is a petroleum economist and energy trading professional with more than 20 years of experience in the oil and gas industry. He began his career at Petronas, where he worked across operations, logistics, and trading, managing products such as fuel oil, LPG, gasoline, crude oil, and naphtha.

Over a 15-year tenure, he handled large-scale international trading portfolios, selling millions of barrels per month to major global buyers.

Baron is now co-founder of INDEX, the International Digital Exchange, where he is focused on applying tokenization and blockchain technologies to modernize oil trading infrastructure.

🌐 website: https://indexlitro.com

☎️ Contact: [email protected]

⚒️ Additional Tools & Resources

🎬 Go backstage and check out my studio:

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🎓 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

🎤 Contact for Lectures and Keynotes

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

Large-scale capital projects sit at the heart of the oil and gas industry, and across all infrastructure sectors (power, petrochemicals, rail, water, telecoms). These projects require tight coordination of people, equipment, and timelines, often under pressure to deliver quickly and safely. Despite heavy investment in planning tools and scheduling systems, the day-to-day reality remains fragmented, with teams working across disconnected systems and making decisions in isolation.

The issue is not a shortage of data, but a lack of connected context. Teams make decisions that work locally, but cannot see the ripple effects across the broader project. When disruptions occur, such as missing materials or shifting priorities, organizations fall back on manual replanning, pulling experts together to work through scenarios. The result is familiar: delays, rework, misalignment, and a persistent focus on reacting to problems rather than anticipating them.

A new approach is emerging that focuses on execution intelligence, not just planning. These systems sit across existing tools, capture real-time context, and use AI to recommend next steps while keeping humans in control. Instead of replacing systems like P6, they enhance them, helping teams understand what matters most today and how decisions impact the wider system. This allows experienced staff to apply judgment more effectively, while also enabling less experienced team members to make better decisions faster.

In this episode, I am speaking with Cali Collins, Chief Product Officer at Optimality, about how fragmented decision-making slows down capital projects and why execution—not planning—is where projects succeed or fail. We explore how AI can surface hidden dependencies, improve decision quality, and help teams navigate complex trade-offs in real time.

👤 About The Guest

Cali Collins is the Co-Founder and Chief Product Officer of Optimality, an AI-native decision infrastructure platform redefining how organizations structure, optimize, and scale decision-making in an era of overwhelming data and competing priorities. She is an experienced product leader with a background spanning AI infrastructure, knowledge systems, estimating, enterprise transformation, and large-scale capital project execution. Cali specializes in translating complex operational challenges into scalable product strategies, bridging business, technical architecture, and real-world execution across both startups and Fortune 50 environments.

⚒️ Additional Tools & Resources

🌐 Website: https://optimalitypro.com 💼 LinkedIN: https://www.linkedin.com/company/optimalitypro/

📧 Email Cali: [email protected]

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🎓 Take My one day course

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🖥️ Blog series: https://digitaloilgas.substack.com/

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💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

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🎤 Contact Me

I speak regularly on these and other topics. Contact me to book a brief call about your upcoming event needs:

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

One thing I've learned from producing lots of videos is that you can't really trust with your own eyes what you see presented to you anymore. Green screens, video editing, and AI are so good now, and so inexpensive, that anyone can create compellingvideo content of scenes that didn't happen in real life.

As a result, I'm now very skeptical of claims that companies make that they don't offer to back up. Consider the 'organic' chicken at the butcher shop. How do you really know that the chicken has led an exemplary life free from chemicals, pesticides, and growth hormones?

Carbon emissions fall into this category. Carbon dioxide, the invisible by-product of engine output and cement making, has become the poster child for energy transition. Today, producers and (some) consumers are expected to not only reduce carbon emissions but account for them with surgical precision. Yet we need to face an uncomfortable fact: carbon data lacks credibility.

It's assembled from fragmented systems, manually reported and manipulated, or derived from engineering models. It's no wonder that financial markets are skeptically treating carbon credits with such low valuations. Carbon accounting is a hot mess.

⚒️ Additional Tools & Resources

🎬 Go backstage and check out my studio:

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🎓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/

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💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

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🎤 Contact for Lectures and Keynotes

I speak regularly on these and related topics. Contact me to book a brief call about your upcoming event needs.

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

Industrial operations have spent decades optimizing for safety, reliability, and uptime. Control systems, sensors, and field equipment were designed to be stable and predictable, often isolated from the outside world. Cybersecurity, by contrast, evolved largely in IT environments, on a separate track, with different tools, assumptions, and incentives.

That separation is no longer holding. Operational technology is becoming more connected, more digital, and more automated. Sensors stream data to the cloud, vendors require remote access, and AI-driven tools increasingly influence operational decisions. At the same time, cyber threats are moving faster, targeting physical systems with the potential for real-world safety and production impacts.

One response is data meshing: combining traditional cyber telemetry with operational data such as vibration, maintenance history, and asset performance to create a richer, more reliable picture of what is really happening inside industrial environments. When these signals are viewed together, anomalies surface faster, false positives drop, and attacks become harder to hide.

In this episode, I'm speaking with Ian Bramson, VP of Global Industrial Cybersecurity at Black & Veatch, and Keon McEwen, Head of Solutions Development for Industrial Cybersecurity. We discuss why the old idea of the air gap is fading, how safety and cybersecurity are converging, what data meshing really means in practice, and why points of operational change are the right moment to rethink cyber risk.

👥 About the Guests

Ian Bramson is Vice President of Global Industrial Cybersecurity at Black & Veatch. He has spent more than two decades working at the intersection of digital transformation, cybersecurity, and critical infrastructure, with prior roles spanning consulting, energy, and operational technology environments. Ian focuses on helping industrial organizations manage cyber risk as systems become more connected and automated.

Keon McEwen is Head of Solutions Development for Industrial Cybersecurity at Black & Veatch. With a background in programming PLCs and operational systems, Keon bridges OT and cyber disciplines, designing practical security solutions that account for how industrial assets actually operate in the field.

🌐 Black and Veatch

🛠️ Additional Tools & Resources

🎬Go backstage and check out my studio

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Book a brief call about your upcoming event here:

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

Oil and gas companies generate enormous volumes of operational, geological, and production data. Despite this abundance, much of that data remains fragmented, inconsistent, and difficult to trust. Teams often spend a significant portion of their time preparing datasets rather than analyzing them. The result is delayed decision-making, inflated costs, and reduced operational agility.

The core complication lies in data quality, data governance, and data readiness. Duplicate records, null values, drift, and structural inconsistencies make it difficult to move quickly from raw data to actionable insight. Asset teams frequently work semi-independently, each rebuilding transformation processes from scratch. Without reliable data foundations, scaling analytics, automation, or advanced modelling becomes difficult and costly.

In this episode, I'm in conversation with Shravan Gunda, CEO of Kaarvi, to discuss how a structured approach to data ingestion, anomaly detection, ETL transformation, and data lineage can reduce time-to-insight from weeks to hours. He outlines how upstream teams can standardize workflows, support governance requirements such as SOC 2, and deploy platforms either on-premises or via SaaS. Clean, trusted data is a prerequisite for accelerating analytics and enabling more advanced digital capabilities.

👤 About the Guest

Shravan Gunda is the CEO of Kaarvi, an enterprise data platform focused on data quality, governance, transformation, and observability. He previously worked in senior technology roles supporting national oil companies and has extensive experience managing large-scale industrial datasets across upstream environments.

🌐 Website: Kaarvi.ai

💼 Shravan Gunda on LinkedIn

📊 Download the white paper: Operationalizing Agentic AI Across The Entire Data Life Cycle

⚒️ Additional Tools & Resources

🎬 Go backstage and check out my studio:

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📘Blog series: https://digitaloilgas.substack.com/

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

Diesel generators have long been the default answer for powering upstream and midstream oil and gas sites. They are familiar, mobile, and deeply embedded in operating practice. Even in regions with abundant natural gas, operators often rely on fleets of diesel gens to run pumps, wireline units, and auxiliary equipment, treating gas as either waste or something to move to market while importing fuel to keep operations running.

That status quo is becoming harder to defend. Diesel is expensive, noisy, logistically complex, and increasingly misaligned with emissions rules, carbon pricing, and community expectations. Operators face growing pressure to cut operating costs, reduce flaring, and lower emissions, while still maintaining reliability in the field. Yet infrastructure change moves slowly, driven more by habit and organizational friction than by technical limits.

In this episode I'm speaking with Michael Lawson, Vice President of Business Development at Enterprise Group, about using stranded or low-value natural gas to electrify well pads and industrial sites. We discuss replacing dozens of diesel generators with a single gas-turbine microgrid, the economics of site electrification, what kinds of gas streams can be used, and why mindset, not technology, is often the real barrier. It's a practical conversation about cost, reliability, emissions, and why electricity is quietly becoming the enabler of digital innovation in the field.

👤 About the Guest

Michael Lawson is Vice President of Business Development at Enterprise Group. Based in Calgary, he has more than two decades of experience across upstream and midstream oil and gas. His current work focuses on site electrification, deploying natural-gas turbines and microgrids to displace diesel power at industrial and energy work sites.

📧 Contact Michael: [email protected]

🌐 https://enterprisegrp.ca

#michaelcjlawson

⚒️ Additional Tools & Resources

🎬 Go backstage and check out my studio:

https://geoffreycann.com/mystudio/

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📘Blog series: https://digitaloilgas.substack.com/

🎧 Podcast: https://geoffreycann.com/broadcast/

💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

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🎤 Contact for Lectures and Keynotes

I speak regularly on these and related topics. Contact me to book a brief call about your upcoming event needs.

👉 https://geoffreycann.com/contact/

⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.