Unconventional oil and gas is massive. Every year, over $100 billion is poured into steel, sand, and water just in Canada and the US. Yet, most of the planning behind these extraordinary investments still runs on Excel spreadsheets. Spreadsheets were fine in the 90s, but today they struggle to handle the complex interdependencies and real-world constraints of modern tight plays.

The hidden cost to the industry is huge. Expensive inefficiencies, wasted capital, and missed opportunities to improve well design and execution. Other industries abandoned spreadsheets long ago when margins got thin, but oil and gas clings to them, and keeps burning the billions.

My guest in this episode is Sean Hervo, a former Shell upstream engineer, who saw firsthand how spreadsheets literally strangle unconventional well planning. He co-founded PrePad, an innovative digital solution, that replaces bulkly and clunky spreadsheets with digital simulation and optimization tools, giving operators the ability to make faster, smarter, more profitable decisions. Sean explains how the industry can finally ditch the spreadsheets and run well delivery like the modern manufacturing system it has become.

👤 About the Guest

Sean Hervo is the CEO and co-founder of PrePad, a Calgary-based technology company that helps oil and gas operators optimize well planning and completions. Before starting PrePad, Sean spent more than a decade at Shell in drilling, completions, capital planning, and supply chain leadership, giving him deep insight into the challenges of unconventional resource development.

Connect with Sean on LinkedIn: Sean Hervo

Learn more about PrePad: https://www.prepad.io

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Oil and gas operations rely on heavy machinery and equipment that perform critical tasks, yet most of this equipment remains disconnected from the digital landscape of cloud computing, analytics, and autonomy. This lack of connectivity leaves operators with higher costs, inefficient maintenance, and limited visibility into how their assets are really performing.

The traditional approach to equipment design is no longer enough. Operators face pressure to improve efficiency, reduce emissions, and cut costs, but without better data and smarter tools, these goals remain out of reach. The industry cannot afford to keep treating its infrastructure as “dumb metal.”

You might think that only big equipment suppliers can make smart metal, but IJACK, a Canadian equipment manufacturer, is proving otherwise. By embedding remote monitoring, cloud connectivity, and AI-driven analytics into its products, the company enables operators to troubleshoot issues without rolling a truck, optimize performance across entire fleets, and gain valuable insight from real-time data.

In this episode, I speak with Dan McCarthy, President of IJACK, about how his team transformed compressors and pumps into intelligent assets, why the industry needs to embrace innovation, and how a small-town company from Saskatchewan is now serving customers around the world.

👤 About the Guest

Dan McCarthy is the President of IJACK Technologies, an oil field equipment company based in Saskatchewan, Canada. With a background in mechanical engineering and a passion for designing practical solutions, Dan has led IJACK in creating scalable, digitally enabled equipment that helps oil and gas operators improve production and reduce costs. IJACK's innovations, including its RCOM remote monitoring platform, are now deployed globally in markets such as Argentina, Kazakhstan, Bahrain, and the United States.

Learn more about IJACK:

🌐 IJACKWebsite

🔗 Dan McCarthy on LinkedIn

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I speak regularly on these and other 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.

Scheduling in oil and gas has long been a weak link. Wells, rigs, frack crews, contractors, and regulators must all line up in precise sequence, but too often the “system” is stitched together with Excel spreadsheets, siloed tools, and a lot of human memory. The result is inefficiencies, costly delays, and endless arguments in daily meetings.

That model is no longer good enough. The complexity of modern operations, coupled with volatile markets and new constraints (from labor shortages to tariffs to water management) is making traditional scheduling tools obsolete. Operators that rely on outdated approaches risk losing millions in wasted time and missed opportunities.

Spying this problem years ago, Actenum, an AI-enabled scheduling platform that treats scheduling not as a collection of dates, but as a living model of operations, set out to correct this problem. The tool captures constraints, integrates with systems of record, forecasts production, and enables scenario planning, in real time. Companies report faster well delivery, reduced conflicts, smarter forecasting, and millions in direct savings.

In this episode, I speak with Owen Plowman, Vice President of Business Development at Actenum, about how smarter scheduling is reshaping oil and gas. We cover real-world client stories, cultural shifts inside organizations, and how AI is opening new optimization opportuntities in planning, turnarounds, and offshore logistics.

👤 About the Guest

Owen Plowman is Vice President of Business Development at Actenum, a software company specializing in AI-enabled scheduling solutions for the energy sector. With a background in computer science, Owen began his career in the defense sector, later joining Oracle during its rapid global expansion. Since 2006, he has focused on applying advanced scheduling technology to oil and gas, helping clients worldwide optimize drilling programs, turnarounds, and offshore operations.

👉 Connect with Owen and Actenum

• Request a Demo
• Watch Actenum DSO in Action
• Download a Brochure

⚒️ Additional Tools & Resources

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

The oil and gas industry generates extraordinary amounts of data from millions of sensors, yet only a tiny fraction, at most  8%, is actually used to inform decisions on complex and valuable assets. Decades of building analytics and machine learning solutions have helped, but they’ve also left companies with a patchwork of siloed systems and “industrial gridlock.”

The arrival of foundation models in late 2022 introduced the possibility of moving beyond one-off solutions. But generic internet-trained models are not suitable for high-risk industrial environments, where accuracy, context, and explainability are essential. The sector needs something different.

Applied Computing is tackling this challenge head-on by creating a foundation model designed specifically for energy. Built to handle time-series data, diagrams, operator logs, and unstructured engineering information, their model emphasizes contextual understanding, explainability, and zero hallucinations.

My guest this week, Dan Jeavons, is President of Applied Computing and former VP of Computational Science and Digital Innovation at Shell. Dan shares his career journey, why foundation models represent a turning point for the industry, and how energy can finally begin to unlock the 92% of data it currently leaves on the table.

👤 About the Guest

Dan Jeavons is President of Applied Computing, a technology company developing foundation models tailored for the energy sector. At Shell, he led global AI initiatives and oversaw advanced research into digital technologies. With over 20 years of experience in consulting and energy, Dan has been at the forefront of applying data and AI to improve business processes, optimize operations, and explore new business models.

LinkedIn: Dan Jeavons

Applied Computing

⚒️ Additional Tools & Resources

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

Water is the unsung workhorse of the oil and gas industry. It's instrumental for generating steam, driving b, lubricating drill bits, flooding reservoirs, and separating oil from oil sands. Historically it’s been cheap, plentiful, and overlooked. As climate pressures mount and scarcity becomes real, water is now emerging as one of the industry’s most critical resources.

Water isn’t just another utility, like power. It's a highly interconnected system. A quick fix in one unit can cause downstream failures, regulatory breaches, or environmental harm. Unlike power, water can be reused. Companies are now wise to the fact that traditional, siloed approaches to water management no longer work.

One solution lies in building holistic, site-wide digital twins of water systems. These models bring together flows, chemistry, capacity, compliance, and infrastructure data into one view, enabling operators to troubleshoot more effectively, run “what if” scenarios, and align operations with ESG commitments.

In this episode, I speak with Gil Maron, a project engineer with FTD Solutions, about his journey from refinery process engineer to water management specialist, why water is local and unique to every site, and how digital twins are helping oil and gas companies cut costs, meet sustainability goals, and avoid costly mistakes.

👤 About the Guest

Gil Maron is a Project Engineer at FTD Solutions, where he focuses on industrial water management, treatment, and analytics. With a background in refinery operations and pharmaceuticals, Gil brings deep expertise in troubleshooting complex process systems. At FTD, he works with oil and gas, semiconductors, and other industries to help clients adopt holistic, data-driven approaches to water use and sustainability.

🔗 Connect with Gil on LinkedIn

🔗 Learn more at FTD Solutions

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🎤 Contact for Lectures and Keynotes

I speak regularly on topics like digital innovation, sustainability, and the energy transition. Book a call to discuss your upcoming event.

⚠️ Disclaimer

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

The industrial sector faces a growing challenge: how to train a rapidly evolving, inexperienced workforce to safely and effectively operate aging and complex infrastructure. Traditional training tools like PowerPoint presentations and passive classroom learning no longer cut it, especially in high-risk environments like oil refineries and offshore rigs.

Enter immersive training platforms—tools that provide guided, interactive learning experiences in virtual environments. These platforms bridge the comprehension gap by enabling engineers to interact with equipment and scenarios in a simulated setting before ever stepping on site. The benefits are substantial: improved cognition, faster onboarding, safer operations, and better cross-functional awareness.

In this episode, I speak with Matt Trubow, Commercial Director of Hidden Creative, where we discuss how their browser-based immersive platform “Simmerse” is transforming how engineering-heavy industries onboard and train personnel. From AI-driven hazard detection to gamified safety walkthroughs, Matt illustrates why immersive tech is not just a nice-to-have but a must-have for the future of industrial training.

👤 About the Guest

Matt Trubow is the Commercial Director and co-owner of Hidden Creative, a UK-based company specializing in immersive training technologies for the engineering sector. With a background in military systems, computer engineering, and complex simulations, Matt brings a practical and deeply technical perspective to digital transformation in industry. Learn more at www.hiddenltd.com or connect with Matt on LinkedIn.

⚒️ Additional Tools & Resources

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⚠️ Disclaimer

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In energy and manufacturing, vast volumes of unstructured data (think OEM manuals, maintenance logs, shift notes, correspondence, procedures), sit largely untapped. For decades, experienced technicians have compensated by carrying critical knowledge in their heads. But with retirements accelerating and fewer seasoned workers on the front line, this model is breaking down. 

New large language learning models that underpin technologies such as Grok and ChatGPT are being trained on this unstructured content to create context-relevant, queryable databases for industry. This technology, referred to as retrieval-augmented generation (RAG), could help unlock hidden knowledge across sprawling document sets.

Early attempts at RAG have certainly improved search, a task that consumes hours of scarce engineering time. However, companies quickly learned that speed and accuracy fall apart at scale, context matters, and lack of trust in the output leaves users frustrated and skeptical. 

The real opportunity lies in pairing RAG with agent-based AI systems designed for complex, asset-intensive environments. By reducing mean time to repair (MTTR), cutting rework, and extending the interval between failures, these solutions directly recover lost production capacity, which is an eight or nine-figure problem in many enterprises. For younger, less experienced workforces, AI tools are a critical equalizer, levelling the field against looming labor shortages.

In this episode, I speak with Mark Fosdike, CEO and co-founder of Datch, about how his company is pioneering AI-driven diagnostic agents for manufacturing and industrial clients. We explore the realities of implementing RAG in high-stakes industries, the economic drivers behind adoption, and why obsessing about customers’ problems is the key to success.

👤 About the Guest

Mark Fosdike is the CEO and co-founder of Datch, an AI company building agent-based diagnostic solutions for asset-intensive industries. With a background in aerospace engineering, shipbuilding, and manufacturing systems, Mark has spent his career navigating the complexities of the industrial value chain. His vision for Datch was born from frustration with traditional maintenance practices and a desire to put AI to work solving real-world challenges on the plant floor.

• LinkedIn Profile

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⚒️ Additional Tools & Resources

• 🎙️ Go backstage and check out my studio: geoffreycann.com/mystudio

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⚠️ Disclaimer

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

Why is a pug named Phoebe likely more qualified than your frontline crew? 

In the oil and gas industry, online training and certification have become the norm. Let's agree it's convenient, cost-effective, and scalable. But what if the people taking that training are subverting the training using AI, or aren’t people at all?

Digital tools, especially generative AI, are now being misused to automate training completion, which undermines the entire compliance system. The same platforms designed to protect workers, assure reliability, comply with regulations, and safeguard the environment may be certifying untrained staff, putting lives at risk and exposing companies to severe legal and financial consequences.

In this episode, Rob Day joins me on the podcast to discuss this hidden crisis. Rob is Managing Director at Cognisense, and a dedicated expert in risk mitigation. From court cases involving industrial explosions to training a pug to complete OSHA 10, Rob shares shocking and true stories from the frontlines of digital risk. He also outlines what companies can do right now to reclaim control over their training systems, ensure legal compliance, and reduce exposure. All managers and supervisors on the front line of industrial work need to be aware of the risks they face. 

#RiskWashing #OHS #SafetyTraining #TrainingthatMatters #AI #WorkplaceSafety #RiskManagement #OilAndGas 

👤 About the Guest

Rob Day is Managing Director of Cognisense, a firm focused on risk mitigation and training validation for industrial operations. His background spans military firefighting, hazardous materials response, legal consulting, and refinery operations. Rob has advised on major incident investigations and regulatory risk across the energy sector. He’s now leading efforts to counter AI-enabled “risk washing” and bring integrity back to digital training systems.

🔗 Connect with Rob on LinkedIn

🌐 Connect with Cognisense

🧰 Additional Tools & Resources

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

The oil and gas industry has invested heavily in new digital technologies, but too many efforts fall short in value delivery. However compelling digital initiatives may seem, they will assuredly flounder if workers are unable or unwilling to adopt them.  The challenge lies in the way asset centric organizations tend to approach change. Leaders often seek rapid results that will contribute to current earnings, while frontline teams struggle with clunky processes and data silos ill-suited for AI, analytics, robotics, connected workers, and many other innovations. Rolling out new tools at scale without winning hearts and minds often leads to resistance, “pilot project hell,” and failed implementations.  So how can energy companies unlock real value from digital transformation? What are the overlooked success factors that determine whether AI and digital deployments thrive or falter? In this episode, I’m joined by David Moore, a digital advisor and AI delivery lead, who reflects on the five critical lessons he wishes he had known earlier in his career. From the power of storytelling, to focusing on people before technology, to the need to start small and scale, to leveraging vendors as partners, and finally the hard truth that data is the real currency, David shares practical insights drawn from frontline experience. ⸻ 👤 About the Guest   David Moore is a well engineer by background with a PhD in mechanical engineering and now a digital advisor and AI leader. Over the past nine years, he has worked on digital transformation across upstream oil and gas, leading projects from Microsoft Teams deployments to AI-driven planning and real-time operations. David brings a unique perspective from seeing the industry’s challenges from both the operator and service company side, with deep experience in building adoption, scaling pilots and designing data foundations all on a global scale.    🌐 Website: www.davidjmoore.com 🛠️ Additional Tools & Resources

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⚠️ Disclaimer

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

The energy industry is having a moment. As assets become digitized, operations increasingly automated, molecules displaced for electrons, and capital more discerning, the biggest challenge is now people, instead of engineering. Specifically, where will the next generation of digitally fluent energy professionals come from?   At the same time, academic institutions, whose mission is to deliver qualified talent, are under grave financial pressure. International student revenue is collapsing, and many programs are out of step with the real-world digital needs of industry. Meanwhile, the workforce is retiring, and young professionals are turning away from careers in oil and gas.   In this episode, Doug Cronk, an investment committee advisor, and former Academic Chair of Financial Services and Fintech at SAIT, sets out how the energy sector can seize this moment. By repackaging existing academic offerings, combining them with data analytics, AI, and energy domain knowledge, and co-creating programs that reflect the needs of a digital energy economy, the energy sector can secure its future talent needs.   Doug argues that industry must lead, by articulating exactly what skills are needed, and why energy companies should present themselves as data-driven innovation platforms, not just extractive enterprises. This episode offers a clear-eyed assessment of the education-industry disconnect, the talent crisis looming over the sector, and the strategic opportunity to build the next generation of digital energy leaders.      👤 About the Guest   Doug Cronk is an experienced board member, investment committee advisor, and former Academic Chair of Financial Services and Fintech at SAIT. His career spans banking, pension fund management, and education, with a specialty in helping institutions adapt to technological and demographic disruption. Today, he sits on multiple boards where he evaluates long-term investment strategies and talent risk in capital-intensive sectors, including energy.   🔗 Doug Cronk on LinkedIn 🛠️ Additional Tools & Resources

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🎤 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.