『Chuck Yates Got A Job』のカバーアート

Chuck Yates Got A Job

Chuck Yates Got A Job

著者: collide.
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Welcome to Chuck Yates Got A Job with Chuck Yates. You've now found your dysfunctional life coach, the Investor Formerly known as Prominent Businessman Chuck Yates. What's not to learn from the self-proclaimed Galactic Viceroy, who was publicly canned from a prominent private equity firm, has had enough therapy to quote Brene Brown chapter and verse and spends most days embarrassing himself on Energy Finance Twitter as @Nimblephatty.2025 Digital Wildcatters, Inc 個人ファイナンス 経済学
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  • Todd Bush
    2025/10/15

    AI is shaking up oil and gas in all the best ways, and Todd Bush from Collide breaks it down like few can. He and Chuck dive into how automation is tackling tedious processes, think regulatory filings like W-10s and G-10s, and how AI is streamlining data extraction for mineral revenue statements. Todd shows off Collide’s enterprise AI system in action, revealing how it saves time, slashes costs, and helps operators make smarter, faster calls in the field. It’s a wild look at how machine learning is making oil and gas operations not just more efficient, but safer and a hell of a lot cooler.


    00:00 - Intro

    00:58 - How Todd and Chuck Met

    05:04 - Lessons from Selling AI Software

    09:42 - Selling Outcomes in AI

    14:05 - Automation of G10s and W10s

    17:00 - AI-Generated W10s Demo

    20:55 - No Human Involvement Required

    23:00 - Revenue Statement Extraction Demo

    27:25 - Where Does AI Stop?

    27:58 - Future of the AI Industry

    32:18 - AI as an Enhancement Tool

    32:50 - Automating Workflows for Strategic Time

    33:07 - Outro



    Join the conversation shaping the future of energy.
    Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.
    Apply today at collide.io

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    34 分
  • ProFrac STARS 2025 with Matt Wilks & Seismos
    2025/10/08

    Real-time frac data isn’t some future concept, it’s here, and it’s changing the way completions get done. Chuck Yates sat down with folks from ProFrac and Seismos to break down how AI, automation, and acoustic sensing are turning traditional fracking on its head. Matt Wilks and Panos Adamopoulos talk about ProPilot 2.0 and how closed-loop fracturing is giving operators instant feedback to fine-tune every stage. Then Larry Carroll and Steven Bourgoyne take it further, showing how machine learning and data-driven workflows are pushing completions toward adaptive, even autonomous, operations. From cutting costs to squeezing every drop of performance, this one’s a look at how the oilfield is starting to feel a lot more like sci-fi.

    Click here to watch a video of this episode.


    Join the conversation shaping the future of energy.
    Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.
    Apply today at collide.io


    Click here to view the episode transcript.

    0:00 Introduction and On-Site Experience
    0:43 Frac Automation Evolution: From ProPilot to Real-Time Adaptation
    2:07 Seismos Partnership: Real-Time Subsurface Data and Closed Loop Fracturing
    5:05 Cost Savings vs Well Performance: What Real-Time Data Enables
    7:10 Breaking Down Engineering Silos with Real-Time Collaboration
    10:06 Supervised vs Unsupervised Closed Loop: Control and Customization
    14:01 Role of AI in Frac Operations and Data Management
    16:11 Case Study: Equipment Optimization and Millions Saved
    18:26 Adaptive Systems: AI + Physics-Based Measurement in Action
    20:49 Human Expertise in an AI World: Replacing or Enhancing?
    23:16 How the ProFrac–Seismos Partnership Came Together
    25:32 Looking Ahead: Fully Autonomous Fields and Future Inventory
    29:35 Frac Performance Measurement: Defining Good vs Bad
    33:11 Customer Collaboration: Building Prescriptive Completions Together
    36:40 Isolating Variables: Why Controlled Change Matters
    40:13 Real-World Results: Cost and Performance Improvements
    42:31 Future Roadmap: More Intelligent, Automated, Efficient Fracs
    45:33 Refracs and Unlocking More Value from Existing Wells
    47:07 Overcoming Customer Risk Aversion to Innovation
    49:06 True Partnerships: EMPs and Service Companies Working Together
    51:24 Rethinking Tribal Knowledge: AI as a Force Multiplier
    53:48 Final Thoughts and Closing Remarks

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    55 分
  • The Secret Data Engineering Behind Industry AI
    2025/09/17

    This episode is packed with big-picture energy talk and some seriously nerdy (but fun) data breakdowns. John Kalfayan from collide. and Chuck start with what’s really happening in oil and gas today before shifting into the challenges of putting AI to work in the field. From there, things get deep: contract dedications, what RAG actually means, how data chunking works, and the never-ending battle with duplicate info. We also weigh the costs of storage, querying, and running models, plus the tradeoffs between RAG and foundational models. If you’ve ever wondered about vector databases, data strategy, or just why we have a rant about sand, it’s all here. By the end, we hit on the human side too: education, privacy, and making sure the right people can access the right data.

    Click here to watch a video of this episode.

    Join the conversation shaping the future of energy.
    Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.
    Apply today at collide.io


    Click here to view the episode transcript.

    00:00 - Intro
    01:51 - Oil and Gas Industry Insights
    06:34 - AI Deployment Challenges
    09:12 - Contract Dedications Explained
    10:32 - Understanding RAG
    12:52 - What is RAG in Data Management
    13:43 - Data Chunking Techniques
    17:17 - Cost Considerations in Data
    18:03 - RAG vs Foundational Models
    19:21 - Vectorized Databases Overview
    23:47 - Managing Duplicate Data
    26:28 - Data Strategy Considerations
    28:24 - Sand Rant
    31:32 - Identifying Gaps in Data
    33:10 - The Cost of Storage
    33:56 - Effective Data Querying
    35:50 - AI Education and Awareness
    37:53 - Privacy Concerns with Language Models
    40:54 - Data Access and Availability

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    https://www.linkedin.com/company/collide-digital-wildcatters

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    45 分
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