How Infrared Spectroscopy Is Changing Industrial Water Diagnostics, an interview with Graham Roeber of Orono Spectral SolutionsOil and water don't mix, but in oil and gas operations, they frequently end up together. Refineries, processing plants, and other industrial facilities routinely test their water streams for hydrocarbon contamination. Historically, this testing has been driven by environmental compliance. The objective is to demonstrate that discharged water meets regulatory standards, rather than to improve the performance of the facility.The problem is that traditional oil-in-water testing can be slow, labour-intensive, and expensive. Results can take hours, making the technology poorly suited to diagnosing leaking equipment or monitoring process performance. Meanwhile, oil escaping into wastewater represents lost product, increased treatment costs, and potentially costly equipment failures. For operators trying to improve efficiency without adding capacity, these losses matter.Infrared spectroscopy offers a different approach. By capturing oil from water samples on a specialized membrane, the technology can measure hydrocarbon concentrations in minutes rather than hours. More intriguingly, the spectral fingerprint of the captured oil can be compared against reference data to help identify the type of oil present. That makes it possible to narrow down the source of contamination, detect emerging problems, monitor process trends, and make better operational decisions.In this episode, I speak with Graham Roeber, CEO of Orono Spectral Solutions (OSS), about how this technology is transforming industrial water diagnostics. We discuss how chemical fingerprints can help maintenance teams locate leaking equipment, why faster testing changes the economics of process monitoring, the challenges of gaining regulatory acceptance, and how AI is making sophisticated chemical analysis more accessible. We also explore emerging applications in desalination, ammonia, corrosion monitoring, and other industrial processes.👤 About the GuestGraham Roeber is CEO of Orono Spectral Solutions (OSS), a company specializing in infrared spectroscopy technology for oil-in-water analysis and industrial water diagnostics.OSS develops analytical solutions that help industrial operators measure hydrocarbon contamination, identify oil types, and gain actionable insights into the performance of their equipment and processes.Website: https://www.ossmaine.com/Graham on LinkedIn: https://www.linkedin.com/in/grahamroeber/#DigitalOilAndGas #InfraredSpectroscopy #WaterTechnology #IndustrialInnovation #OilAndGas #OronoSpectralSolutions-----------------------------------------------------⚒️ Additional Tools & Resources📚 My books: https://geoffreycann.com/book/🎬 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/podcast/💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/🐦 X: https://x.com/geoffreycann👉 https://geoffreycann.com/contact/-----------------------------------------------------⚠️ DisclaimerThe views expressed in this podcast are my own and do not constitute professional advice.

Oil and gas companies have invested billions in SCADA, sensors and other technologies to understand what's happening with their equipment. Yet field workers still spend huge amounts of time driving to remote assets simply to inspect them. Experienced operators can see, hear and sense things that conventional instrumentation misses.

Those blind spots have consequences. Equipment problems can go unnoticed until they become failures. Workers make unnecessary truck rolls. Maintenance becomes reactive. And with experienced field workers becoming harder to find, the industry needs a better way to understand what's happening across thousands of pieces of equipment.

Physical AI offers another approach. New edge computing technologies can continuously interpret sound, vibration, vision, temperature and other signals right at the equipment. Add AI decision-making and the result is much greater visibility into equipment condition, earlier warning of problems, fewer unnecessary field visits and more targeted maintenance.

In this episode, I'm in conversation with Alex Hawkinson, founder and CEO of BrightAI. We talk about stick-on AI sensors, edge computing, connected-worker technology, predictive maintenance and robot dogs. We also explore where this could ultimately lead: an oil field that continuously observes its own condition, decides what needs attention and eventually sends robots to take action.

👤 About Alex Hawkinson

Alex Hawkinson is the founder and CEO of BrightAI, a Physical AI company focused on the infrastructure and essential services that modern society depends on. Alex previously founded SmartThings, the connected-home technology company acquired by Samsung. With BrightAI, he is applying his experience in connected devices, AI and edge computing to energy, power, water, transportation and other physical infrastructure.

BrightAI: https://bright.ai/

BrightAI for Oil & Gas: https://bright.ai/industries/oil-gas/

Stateful OS: https://bright.ai/stateful-os/

Connect with Alex Hawkinson: https://www.linkedin.com/in/ahawkinson/

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
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🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
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🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

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

#PhysicalAI #IndustrialAI #OilAndGas

AI can help us quickly when we need to make decisions. But when executives are making high-stakes decisions about pipelines, energy infrastructure, capital investments or geopolitical risk, a quick answer isn't necessarily a good answer. Insight into the decision is fragmented across organizations, external information is difficult to assess, and misinformation, competing interests and human bias all complicate the picture.

General-purpose AI tools can sort of help solve this problem. I find that they are designed to produce a fast answer, not necessarily to work systematically through a complex problem using rigorous analytical techniques. Strategic decisions need something more thorough: clearly defined problems, credible sources, competing hypotheses, scenario analysis, bias checks, traceable reasoning and the ability to revisit an assessment as circumstances change.

Structured AI offers a different approach. Instead of replacing the human analyst, it combines AI with formal intelligence methodologies and human judgement. The result is analysis that can be challenged, explained and refreshed as conditions evolve. It's so much more satisfying than an AI-generated answer.

In this episode, I talk with Chris Coupal of Foresight Reports about Nexus, the company's structured intelligence platform. I wanted to know why traditional approaches to intelligence are faring with today's complicated information environment, how structured AI differs from tools such as ChatGPT, why deliberately searching for contradictory evidence matters, the continuing importance of the human analyst, and how this approach could change strategic decision-making across energy and other industries.

👤 About The Guest

Chris Coupal is Chief Operating Officer of Foresight Reports. His background includes 16 years in the Canadian military as a signals officer, including service in Afghanistan, as well as experience in mining and oil and gas.

At Foresight Reports, Chris works with a team of analysts focused on intelligence and forecasting. The company has traditionally specialized in activism intelligence and social licence to operate and is now applying structured analytical techniques and AI to a broader range of strategic problems through its Nexus platform.

💼 Chris Coupal 👉 https://www.linkedin.com/in/chris-c-828702/
🌐 Foresight Reports 👇
https://www.linkedin.com/company/foresight-reports/

https://foresightreports.com

📧 [email protected]

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
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👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
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🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

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

Digital innovations are creating huge value for oil and gas, but digital landscape is setting the industry up for problems down the track. 

Think of them as digital time bombs. Quantum computing will expose encrypted data that companies believe is safely protected. Legacy on-premise software is fast becoming difficult and costy to support as investment capital moves elsewhere. And the industry's shrinking talent pipeline will leave companies without enough people, particularly people with the digital skills needed to replace retiring workers.

In this episode, I look at four digital time bombs facing oil and gas: decrypting stolen data, hacking live business communications, aging software, and the shrinking talent pool. The exact timing of these problems is uncertain, but that's the nature of a time bomb. Companies that recognize them early still have time to mitigate the impacts, or perhaps defuse them entirely.

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
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👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
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🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

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

 Energy superpower is a fine marketing slogan. In fact, lots of countries have stated their ambition to become the next energy superpower, but oddly, no one spells out what that actually means.

Fortunately, there is a rigorous academic definition for energy superpowerhood, which ranks the EU, the U.S., and China as the world's true energy superpowers. All others are pretenders, Canada included, despite its obvious oil and gas resources. 

What would it take to become a true energy superpower, and is now strategically the right time to pursue such a game plan?

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
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🔗 Connect with Me:

🖥️ 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.

Industrial AI is into a new phase. Agentic AI is software that can plan, reason, learn, and act with far greater independence than traditional automation. While the technology is advancing quickly, the oil and gas industry faces unique technical, regulatory, and operational challenges that make full autonomy far less straightforward. 

In this episode, I share what agentic AI actually is, why it differs from conventional automation, and why the industry's aging assets, fragmented data, safety requirements, cybersecurity concerns, and accountability standards create real barriers to  adoption. 

I also share where I believe agentic AI can deliver the greatest value today: reducing the cognitive burden on engineers by assisting with planning, monitoring, coordination, and decision preparation while keeping humans squarely in the loop. The future is a function of humans plus AI, instead of the gloomy either/or we hear. 

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
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🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
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📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

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

It's sad to say but wars create opportunities to apply new technologies in novel ways as the combatants seek to gain advantage over each other. The US-Israeli War on Iran is a case in point. If you've been monitoring the public airwaves about the war, you'll have picked up hints about the role that digital innovations are playing in the crisis. These signals in and of themselves are almost always overshadowed by the big kinetic actions, such as bombings and destruction.

Collect them together, project them forward, and they reveal how the world will likely be different when the hostilities abate.

In this podcast I share several insights that I believe have significant bearing on the future of oil and gas infrastructure, not just in the Gulf area but globally wherever there is the potential for conflict.

⚒️ Additional Tools & Resources

🎬 Go backstage and check out my studio:
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🔗 Connect with Me

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⚠️ Disclaimer

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

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:
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🎓 Take my one day digital strategy training course for oil and gas:
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🔗 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:
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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

⚠️ 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:
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🔗 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.