From Volume to Value: Optimizing Daily Cashflow in the Modern Oilfield

In this webinar episode of The Empirical Energy Podcast, host Mark Smith is joined by Casey Patterson, Co-Founder of Avenirre, for a deep dive into how upstream oil & gas operators are shifting from volume-based decision-making to daily, well-level cash flow optimization.

As market pressure increases and margins tighten, traditional volumetric metrics are no longer enough. This conversation explores how forward-looking economics, real-time data, and financial visibility at the well level are changing how modern operators manage production, expenses, and profitability.

• Why barrels alone don’t equal profitability • How daily cash flow reporting outperforms lagging, month-end data • The hidden risks of the unit fallacy and uneconomic wells • How operators uncover recurring cash flow leaks • The role of real-time pricing, forecasting, LOE visibility, and reserves • Why forward-looking analytics beat rear-view reporting • How Avenirre’s platform is implemented—and why customers see impact fast

This episode is especially valuable for: ✔️ Upstream operators ✔️ Production & reservoir engineers ✔️ Energy finance and asset management teams ✔️ Digital oilfield and energy technology leaders

00:00 Introduction to The Empirical Energy Podcast 00:50 The Shift in Energy Metrics 02:27 Optimizing Cash Flow in Energy Companies 04:16 Daily Economic Reporting & Automation 07:27 Case Studies & Real-World Applications 12:16 Implementation & Customer Success 17:00 Live Q&A with Mark Smith & Casey Patterson 22:26 Challenges in Adopting Avenirre 23:22 Data Migration Process 23:49 Morning Reports & Multidisciplinary Use 25:57 Unique Platform Features 26:51 Customer Support & Success Model 27:33 Integration & Cost Efficiency 30:13 Real-Time Data & Dashboard Fatigue 36:57 Cash Flow Management & Reserve Reports 40:57 Conclusion & Next Steps

🎧 Subscribe to The Empirical Energy Podcast for more conversations on verified energy, digital oilfield innovation, and real-world solutions shaping the future of oil & gas. 👍 If this episode helped you, like, comment, and share it with your team.

Operators often see groups of wells (field/unit/route) as profitable while subsets quietly lose money—masked by aggregated views, declining production, flat pricing, rising LOE, and forward financials limited to twice a year.

Manual allocation of production & expenses takes weeks or months, so detailed analysis gets delayed or skipped.

What if you could automate it and get true well-level daily cashflow visibility—instant forecasts, targets, and alerts?

This educational live webinar where Casey Patterson (Founder & CEO of Avenirre, formerly XTO Energy) explores the challenge and demonstrates a practical approach built by former XTO and EOG upstream professionals.

You'll discover:

• Why aggregated economics hide underperforming wells and recurring losses
• How automation delivers well-level forward cashflow forecasts and alerts—without weeks of manual work
• Key signals: negative cashflow wells, volume shortfalls, LOE variances/overages, underpayments
• Real-world case studies (shown live by Casey, naming operators) with clear outcomes from addressing these issues
• Benefits of consolidated data, forecasting, and visibility for proactive decisions

How Measured, Verified Data Is Reshaping Global Energy Markets

In this episode of The Empirical Energy Podcast, host Mark Smith is joined by David Conley, Co-Founder of CleanConnect.ai, for a deep dive into how empirical AI is transforming the energy industry from the ground up.

They explore how Clean Connect’s platform combines direct measurement, first-principles engineering, AI, and blockchain to replace emission factors with verifiable truth—creating a flexible, auditable system for modern energy production, sustainability reporting, and trading.

This conversation goes beyond theory, featuring real-world case studies from some of the world’s largest energy producers. Mark and David unpack how empirical data is driving measurable ROI across operations, emissions management, safety, and production optimization, while unlocking new premium markets for verified energy.

You’ll also hear how multi-certification frameworks like Prove Zero, blockchain-based Energy Attribute Certificates (EACs), and partnerships with global energy traders such as Gunvor are enabling new energy products tailored for hyperscalers, AI data centers, and global buyers.

From methane mitigation and remote operations to AI-driven orchestration layers and direct combustion measurement, this episode reveals why measured and verified energy is no longer optional—it’s becoming the gold standard.

🎧 Whether you’re an energy producer, trader, operator, or technology leader, this episode offers a clear look at where the industry is heading—and how to prepare for what’s next.

00:00 – Blockchain trading and the origins of empirical verification 00:03 – Why Clean Connect became a source of truth in noisy data environments 00:12 – Moving from emission factors to first-principles measurement 00:30 – Crew Zero and direct measurement at the source 00:37 – Project Vulcan and real-time combustion measurement 01:02 – Why energy and AI are now inseparable 01:45 – Welcome to The Empirical Energy Podcast 02:03 – Global market trends shaping the future of energy 02:45 – Introducing Empirical.ai: the AI operating system for energy 03:30 – Real client case studies and measurable ROI 03:45 – The evolution of Clean Connect beyond methane mitigation 04:56 – Operations, sustainability, and market-driven outcomes 08:12 – Restoring trust through empirical data 09:18 – Integrating operations, sustainability, and trading 10:26 – Highlights from the Empirical Energy Conference 11:03 – Client feedback and new product innovation 12:09 – Remote operations, safety, and workforce augmentation 14:00 – The Integrated Operations Center explained 19:22 – Solving the data integration problem at scale 20:47 – Prove Zero and multi-certification flexibility 25:06 – Overcoming data complexity with first principles 29:08 – Partnerships, hyperscalers, and new energy markets 32:13 – Blockchain-enabled trading and Energy Attribute Certificates 33:10 – The future of empirical energy 35:01 – Final thoughts and call to action

🎙️ Revolutionizing Gas Leak Detection with Visual AI & Machine Learning

In this episode of The Empirical Energy Podcast, host Mark Smith sits down with Jae Yoon Chung, Machine Learning Engineer at Clean Connect, to explore how visual AI is transforming gas leak detection in the energy industry.

Jae breaks down the real-world challenges of detecting methane and gas leaks using vision-based models — especially in harsh outdoor environments with wind, rain, snow, and limited edge-device compute. He introduces a breakthrough approach called channel stacking, a method that captures gas movement using just three consecutive frames to dramatically improve detection accuracy while reducing computational load and false alarms.

The conversation goes beyond theory, offering a behind-the-scenes look at how AI models are trained, optimized, and deployed at the edge — and where the technology is headed next. From edge computing to large language models (LLMs) and object-level incident classification, this episode highlights how AI, blockchain, and verification are reshaping the future of global energy markets.

⚡ If you work in energy, AI, emissions monitoring, or industrial technology, this episode is a must-listen.

⏱️ Episode Chapters

00:00 – Introduction to The Empirical Energy Podcast

00:57 – Meet the Guest: Jae Yoon Chung from Clean Connect

01:39 – Machine Learning Challenges in Energy Environments

03:44 – Innovations in Visual Gas Leak Detection

07:29 – Technical Deep Dive: Channel Stacking Explained

14:23 – The Future of Visual AI & LLM Integration

18:53 – Final Thoughts & Call to Action

🎧 Listen & Watch

▶️ YouTube: https://youtu.be/WOejlQSdr_g

🎙 Apple Podcasts: https://podcasts.apple.com/us/podcast/the-empirical-energy-podcast/id1822839881

💬 If this episode sparked new ideas or questions, join the conversation.

👉 Subscribe, rate the show, and share this episode with someone working in energy, AI, or climate tech.

👉 Drop a comment and tell us: Where do you see the biggest opportunity for AI in energy today?

#EmpiricalEnergyPodcast #VisualAI #MethaneDetection

#MachineLearning #EdgeAI #EnergyTech

#ClimateTech #IndustrialAI #ComputerVision

Uncovering the Future of Methane Emissions Management with Thomas Fox

In this episode of The Empirical Energy Podcast, host Mark Smith welcomes Thomas Fox, President of Highwood Emissions, to unpack the mechanisms reshaping global energy markets.

The conversation explores the industry’s shift from “alternative energy” toward measured, verified energy as the new gold standard—powered by empirical data, evolving regulations, and emerging verification frameworks. Thomas shares his journey from PhD research in methane emissions to building Highwood Emissions and launching the Emissions Intelligence Platform (EIP), a software solution designed to help operators create measurement-informed inventories at scale.

Together, they dive into the realities of methane reporting: reconciliation challenges, OGMP Level 5 requirements, EPA and Colorado regulatory approvals, and why harmonization across global reporting frameworks remains one of the industry’s biggest hurdles. The episode closes with a forward-looking discussion on how collaboration, technology, and credible data may finally unlock real market incentives for low-methane energy.

🎧 If you work in energy, ESG, emissions reporting, or climate compliance, this episode offers a clear look at where the industry is heading—and what it will take to get there.

⏱️ Episode Chapters

00:00 Introduction to the Empirical Energy Podcast

00:55 Meet Thomas Fox: Highwood Emissions

02:51 The Journey to Methane Mitigation

04:54 Building Measurement-Informed Inventories

08:27 Challenges and Innovations in Methane Reporting

18:34 The Future of Emissions Reporting

25:02 Closing Thoughts and Call to Action

✔️ Listen to the full episode to understand how methane measurement is evolving

✔️ Subscribe to the Empirical Energy Podcast for more conversations at the intersection of energy, AI, and climate policy

✔️ Share this episode with colleagues navigating methane reporting, OGMP, or ESG compliance

✔️ Join the discussion—what do you think will finally drive harmonized emissions reporting?

#EmpiricalEnergy #MethaneEmissions #EnergyTransition #OGMP #ClimateReporting #EnergyMarkets #OilAndGas #ESG #CarbonAccounting #Decarbonization #ClimateTech #EnergyPolicy #LowMethaneGas #Sustainability

In this episode of the Empirical Energy Podcast, host Mark Smith sits down with Zach Orlin of Clean Connect.ai to explore how digital twins, AI, blockchain, and verification technologies are redefining how energy is measured, monitored, and managed.

The conversation dives deep into Luminary, a digital twin platform that enables teams to virtually map energy sites, plan monitoring equipment placement, and integrate real-time data into operational models before anything is deployed in the field. Zach explains how Luminary connects with Promax process simulation models, remote monitoring systems, and verification frameworks to reduce costs, improve efficiency, and eliminate blind spots in energy operations.

This episode also explores what’s next—from augmented reality overlays to smarter, more empirical energy markets where measurement, transparency, and real-time intelligence become the new standard.

If you’re working in energy, infrastructure, emissions monitoring, or digital transformation, this episode offers a clear look at how legacy systems are being replaced by data-driven, verifiable solutions.

⏱ Episode Chapters

00:00 Introduction to Empirical Energy

00:44 Meet Zach Orlin (Clean Connect.ai)

02:23 Digital Twins & the Luminary Platform

05:05 Virtual Site Mapping & Monitoring Design

11:11 Advanced Capabilities & Future Applications

18:13 Empirical Energy’s Broader Impact & Wrap-Up

🎧 Subscribe for conversations on energy verification, AI, blockchain, and the future of global energy markets.

#EmpiricalEnergy #DigitalTwins #EnergyTechnology #EnergyInnovation #AIinEnergy #BlockchainEnergy #EnergyMarkets #RealTimeMonitoring #EnergyManagement #CleanTech #IndustrialAI #EnergyTransition #OperationalIntelligence

In this episode of the Empirical Energy Podcast, host Mark Smith is joined by Deb Ryan from Geo Financial to discuss the seismic shift in global energy markets. They dive deep into how verification via blockchain, coupled with AI, is reshaping energy systems. Deb shares insights on her background, the methane performance certificates, and the integration of satellite data with real-time monitoring to achieve market solutions. This rich conversation touches on the evolving role of methane detection, the synergy between different technologies, and the economic drivers for emission reduction, while also offering a preview of how data fidelity is revolutionizing the industry. Tune in to find out how companies can leverage these innovative solutions for a sustainable future.

00:00 Introduction to the Empirical Energy Podcast

00:45 Meet Deb Ryan from Geo Financial

01:05 Navigating Carbon: Deb's Podcast and Background

01:40 Methane Performance Certificates and S&P Global

04:32 Challenges in Methane Detection and Solutions

05:16 Geo Financial's Satellite Data and Methane Detection

07:37 Combining Satellite and Ground Data for Better Accuracy

19:28 Market Solutions and Certified Natural Gas

21:31 The Future of Energy Trading and EACs

24:40 Conclusion and Call to Action

Don’t forget to:

👍 Like this if energy markets matter to your world

🔔 Subscribe to follow the people shaping the future of energy

💬 Share your biggest insight—traders and operators learn from each other

🔗 Pass this to someone who works behind the scenes of our energy system

#EnergyMarkets

#MethaneMonitoring

#ClimateTech

#CarbonMarkets

#EnergyTransition

#BlockchainEnergy

#AIinEnergy

#VerifiedEnergy

#CertifiedGas

#MethaneReduction

Gas scheduling, pipeline data, arbitrage, LNG globalization, AI-powered tariffs, and the rapid shift toward reliable low-carbon fuel — this conversation with Jay Bhatty shines a light on how the natural gas market is being rewritten from the inside out.

Jay shares his path from Enron to building NatGasHub into a core scheduling platform for North America, why data is the real commodity traders fight over, and how AI data centers are quietly reshaping the future of energy reliability.

Key Discussion Points

• 00:00 – Opening: the silent revolution in verified energy
• 00:44 – Jay’s journey: India → Ohio → Enron
• 02:25 – Early lessons from Enron alumni & top-tier traders
• 04:10 – Electricity shortages and why energy became Jay’s obsession
• 05:18 – The pain point that sparked NatGasHub
• 06:52 – Turning 50 pipeline websites into one streamlined platform
• 08:40 – Why empirical pipeline data gives traders an edge
• 10:22 – Automation: from whiteboards to real-time digital scheduling
• 12:15 – Arbitrage opportunities and information asymmetry
• 14:10 – The motivation behind Nat Gas Millions
• 15:55 – What new traders misunderstand about the physical market
• 17:48 – “All the money is made in the margins” — real trading war stories
• 19:35 – How geopolitical shocks hit energy markets
• 20:48 – LNG and the rise of natural gas as a global commodity
• 22:36 – AI data centers and the real priorities behind their power demand
• 24:18 – Why big tech is turning toward natural gas infrastructure
• 25:52 – The challenge of tracking low-carbon gas molecules
• 27:05 – Advice for new traders on mastering risk
• 28:20 – Using AI to decode 500-page pipeline tariffs
• 29:10 – Where to find Jay’s book & how to work with NatGasHub

If you're in trading, infrastructure, energy technology, or you're trying to understand how physical markets actually work, this episode gives you the “behind the curtain” view.

Don’t forget to:

👍 Like this if energy markets matter to your world

🔔 Subscribe to follow the people shaping the future of energy

💬 Share your biggest insight—traders and operators learn from each other

🔗 Pass this to someone who works behind the scenes of our energy system

#EnergyTrading #NaturalGas #AIandEnergy #EnergyTech #LNG #CommodityTrading #NatGasHub #PipelineData #DataCenters #EmpiricalEnergy #LowCarbonEnergy #EnergyInfrastructure