Why do some child wells succeed, and others fall short? In this episode of The Novi AI Roundup, we explore how machine learning is helping engineers better understand the impact of parent well depletion on infill performance. Drawing from the technical paper “Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach”, we discuss how depletion varies by parent strength, geometry, and timing, and how ML is helping operators design smarter spacing plans and reduce future production losses.This podcast episode is based on the technical paper “Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach”, authors: Dillon Niederhut, Alexander Cui. Download the full paper here.

Why do some refracs work, and others fall flat? In this episode of The Novi AI Roundup, we explore how machine learning is helping engineers identify the production drivers behind successful refracs in the Williston Basin. Drawing from the technical paper “Revealing the Production Drivers for Refracs in the Williston Basin”, we analyze the impact of spacing, pressure, and prior completions, and show how AI is unlocking new life from old wells.This podcast episode is based on the technical paper “Revealing the Production Drivers for Refracs in the Williston Basin”, authors: Alexander Cui, Tim Gilbertson, Dillon Niederhut. Download the full paper here.

How much does landing zone really matter? In this episode of The Novi AI Roundup, we explore how precision targeting is reshaping development planning in the Midland Basin. Drawing from the technical paper “Enhancing Production Efficiency: The Impact of Precision Targeting in the Midland Basin”, we break down how machine learning reveals meaningful performance differences within benches, and how optimizing targeting decisions can unlock over $2 million in additional value per well.This podcast episode is based on the technical paper “Enhancing Production Efficiency: The Impact of Precision Targeting in the Midland Basin”, authors: S. Christian, T. Cross, J. Garzon. Download the full paper here.

What happens when you delay development of the second bench? In this episode of The Novi AI Roundup, we explore the production impacts of postponing secondary bench wells in the Midland Basin. Drawing from the technical paper “Forecasting Production Loss for Delayed Secondary Bench Development in the Midland Basin”, we show how machine learning isolates the effects of timing, revealing that delays can reduce child well performance by as much as 40%. From Wolfcamp to Lower Spraberry, this episode unpacks how timing, pressure, and depletion combine to reshape development outcomes.This podcast episode is based on the technical paper “Forecasting Production Loss for Delayed Secondary Bench Development in the Midland Basin”, authors: A. Cui, D. Niederhut, B. Davis. Download the full paper here.

Why is it so hard to forecast young wells in the Permian? In this episode of The Novi AI Roundup, we explore the challenges of early-life production forecasting, and how machine learning can outperform traditional decline curve models when data is sparse and variable. Drawing from the technical paper “Enhancing Production Forecasting Accuracy: A Machine Learning Approach in the Permian Basin”, we compare ML vs. Arps across a wide range of well types, and reveal what’s really happening in months 3 through 12.This podcast episode is based on the technical paper “Enhancing Production Forecasting Accuracy: A Machine Learning Approach in the Permian Basin”, authors: K. Sathaye¹ S. Iceton, T. Cross. Download the full paper here.

What’s really happening in the Midland Basin today? In this episode of The Novi AI Roundup, we take a machine learning–driven look at well performance trends, spacing dynamics, and what the data says about the basin’s future. Drawing from a URTeC paper, we explore how operator strategies are evolving, why inventory quality is under more scrutiny, and where ML forecasts diverge from the headlines.This podcast episode is based on the technical paper “Analyzing Midland Basin Well Performance and Future Outlook with Machine Learning”, authors: Brandon Myers, Ted Cross, and Alexander Cui. Download the full paper here.

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.