What does the Dallas Fed Energy Survey really tell us? In this episode of The Novi AI Roundup, we break down the Q3 2025 results and why they mark a turning point in industry sentiment. Drawing from the Novi Intelligence report “Dallas Fed Energy Survey Q3 2025: Key Takeaways and Analysis”, we explore what falling optimism means for activity levels, how operators are responding across oil and gas, and why understanding sentiment is key to planning ahead.This podcast episode is based on the Novi Intelligence report “Dallas Fed Energy Survey Q3 2025: Key Takeaways and Analysis”, written by Robert Polk and Tim Chan. The report was published in October 2025. Download the full report here.
How well do type curves hold up in complex gas plays? In this episode of The Novi AI Roundup, we break down a comparative study of machine learning vs. traditional forecasting methods in the Appalachian Basin. Drawing from the technical paper “Machine Learning vs. Type Curves in the Appalachian Basin: A Comparative Study”, we explore how model performance varies across well vintages, spacing conditions, and depletion settings, and why Appalachia may be the ultimate stress test for forecast accuracy.This podcast episode is based on the technical paper written by A. Cui, A. Yanke, T. Dao, P. Ye, T. Cross, B. Davis. Download the full paper here.
Is the Uinta Basin the next great U.S. oil play? In this episode of The Novi AI Roundup, we explore what makes Uinta unique, and why it’s drawing renewed attention from engineering teams and capital planners. Drawing from our URTeC paper “Unpacking the Uinta Basin: the next great oil play?”, we discuss how ML is helping to decode complex geology, spot development trends, and forecast performance in one of the most underappreciated basins in the Lower 48.Authors: J. Sigler, L. Fidler and, T. Cross. The paper was presented at URTeC. Download the full paper here.
How do you build reliable type curves in one of the world’s most geologically complex shale plays? In this episode of The Novi AI Roundup, we explore how machine learning is helping engineering teams in Argentina’s Vaca Muerta formation generate better type curves and improve field development planning. Drawing from our URTeC paper “Using Machine Learning and Data Analytics to Improve Type Curve Generation and Optimize Field Development Planning in Argentina's Vaca Muerta Formation”, we discuss how AI can de-bias curves, incorporate parent/child effects, and scale insight across operators and acreage blocks.This podcast episode is based on the technical paper “Using Machine Learning and Data Analytics to Improve Type Curve Generation and Optimize Field Development Planning in Argentina's Vaca Muerta Formation”, authors: David Delgado, Charles Kosa, and Peter Zannitto. Download the full paper here.
What if your forecasted uplift isn’t real? In this episode of The Novi AI Roundup, we explore how standard machine learning models can mislead engineers by overestimating production drivers due to bias in the data. Drawing from our URTeC 2025 paper “When Numbers Lie: De-Biasing Horizontal Well Production Datasets with Causal Forecasting”, we discuss how causal AI helps separate correlation from causation, revealing the true impact of spacing, depletion, and geology. From false uplift to masked degradation, this episode is about building models engineers can trust.
This podcast episode is based on the technical paper “When Numbers Lie: De-Biasing Horizontal Well Production Datasets with Causal Forecasting”, authors: Dillon Niederhut and Kiran Sathaye. The paper was presented at URTeC 2025. Download the full paper here.
What does inventory look like beyond the Permian? In this episode of The Novi AI Roundup, we explore how machine learning is helping engineers forecast longer-duration inventory across Rockies unconventional plays. Drawing from our URTeC 2025 paper “Using Machine Learning To Forecast Longer Duration Inventory Across Rockies Unconventional Plays”, we discuss how AI models built for variability and data scarcity are improving confidence in under-drilled areas, and why forecasting uncertainty is critical when inventory becomes strategy.This podcast episode is based on the technical paper “Using Machine Learning To Forecast Longer Duration Inventory Across Rockies Unconventional Plays”, authors: B. Myers and G. A. Quintero. The paper was presented at URTeC 2025. Download the full paper here.
How much does development design affect production outcomes? In this episode of The Novi AI Roundup, we unpack how small changes in spacing, stacking, and timing can lead to big differences in recovery. Drawing from our URTeC 2025 paper “A Bottoms-Up Analysis of Development Scenarios in the Midland Basin Using Machine Learning”, we explore how engineers are using physics-informed ML to evaluate tradeoffs across thousands of pads, and why bottoms-up modeling is changing how teams plan and forecast.
This podcast episode is based on the technical paper “A Bottoms-Up Analysis of Development Scenarios in the Midland Basin Using Machine Learning”, authors: Dillon Niederhut and Brandon Myers. The paper was presented at URTeC 2025. Download the full paper here.
In this episode of The Novi AI Roundup, we go beyond traditional metrics like “feet between wells” or “wells per section” to explore a more physical, multi-dimensional way to teach spacing to machine learning models. Drawing from the URTeC 2025 paper “Describing Well Spacing: Dimensionality at Work”, we dive into how techniques like Voronoi tessellations, nonlinear transformations, and causal modeling can better describe the reservoir neighborhood, leading to more realistic forecasts and clearer insight into spacing degradation. From lateral to vertical interference, this episode rethinks what it means to teach physics to a machine.This podcast episode is based on the technical paper “Describing Well Spacing: Dimensionality at Work”, authored by Kiran Sathaye, Dillon Niederhut, and Alexander Cui. The paper was presented at URTeC 2025. Download the full paper here.
How do you forecast production in basins with limited historical data? In this episode of The Novi AI Roundup, we explore how transfer learning, a cutting-edge machine learning technique, is helping operators unlock the potential of frontier basins. We dive into the challenges of data scarcity, model generalization, and how learnings from mature plays can guide decisions in emerging ones. From the Powder River to beyond, this episode is a must-listen for anyone rethinking where and how we deploy AI in E&P.This podcast episode is based on the technical paper “Can Transfer Learning Be Used to Forecast Production in Frontier Basins? A Case Study from the Powder River Basin”, co-authored by Dillon Niederhut and Gabriel Quintero. The paper was presented at URTeC 2025. Download the full paper here.
How close is too close when spacing horizontal wells? In this episode of The Novi AI Roundup, we break down one of the industry’s most important questions: well spacing. Drawing from our URTeC 2025 paper “Optimizing Well Spacing to Maximize Horizontal Well Performance and Recovery”, we explore how machine learning reveals the balance between tighter spacing, production uplift, and long-term recovery. From Wolfcamp to Spraberry, discover why one-size-fits-all spacing doesn’t work, and how AI is helping operators maximize recovery and economic performance.
This podcast episode is based on the technical paper “Optimizing Well Spacing to Maximize Horizontal Well Performance and Recovery”, co-authored by Ahmed Alzahabi, Alexander Trindade, Ahmed Kamel, and Kiran Sathaye. The paper was presented at URTeC 2025. Download the full paper here.
The unconventional oil and gas industry relies heavily on publicly available production data for environmental impact assessments, economic evaluations, and regulatory decision-making. However, a comprehensive analysis across over 10,000 tight oil wells in four states reveals alarming discrepancies between public and proprietary water production data that fundamentally undermine the reliability of models used for critical industry and policy decisions. These findings challenge the validity of numerous published forecasts and highlight the urgent need for improved data collection and reporting standards.
This blog post is based on the technical paper “The Reliability of Public Water Production Data for Tight Oil Wells”, co-authored by Frank Male¹˒², Ian Duncan², and Kiran Sathaye. The paper was presented at URTeC 2025. Download the full paper here.
While data-driven reservoir characterization has gained traction among operators, most analyses remain constrained to operator-specific acreage or single-basin datasets. This limited scope may introduce systematic bias in feature importance estimation, particularly for geological parameters. This study leverages a comprehensive dataset of 100,000 horizontal wells across seven major U.S. basins to evaluate the robustness of machine learning approaches in identifying true production drivers and their interactions.
This episode is based on the technical paper ” Cross-Basin Analysis of Production Drivers: Insights from Machine Learning Applied to 100,000 Unconventional Wells” presented at URTeC 2025. Download the full paper here.
The Novi AI Roundup
Welcome to The Novi AI Roundup, the podcast that brings you sharp insights, bold conversations, and repurposed gems from our most impactful content at Novi Labs. Whether it's AI-powered forecasting, the latest in energy innovation, or the future of reservoir engineering, we’ve got the mic on what matters.
Each episode transforms our internal know-how, blog gold, and field-tested wisdom into candid discussions. Expect punchy takes, no-fluff breakdowns, and the occasional cowboy hat.