The Novi AI Roundup

Can you accurately forecast water production before a well is drilled? In this episode of The Novi AI Roundup, we explore how machine learning is improving water production forecasting in the Williston Basin. Drawing from the technical paper “Predicting Water Production in the Williston Basin using a Machine Learning Model”, we examine how ML identifies the geological and operational drivers of wa...

How does spacing impact well performance over time? In this episode of The Novi AI Roundup, we explore how machine learning helps quantify the long-term effects of interwell spacing in unconventional reservoirs. Drawing from the technical paper “The Impact of Interwell Spacing Over Time: A Machine Learning Approach”, we examine how spacing influences production decline, recovery, and well interfer...

How do you separate operator skill from rock quality? In this episode of The Novi AI Roundup, we explore how predictive machine learning models are being used to benchmark operator performance in the Williston Basin. Drawing from the technical paper “Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model”, we examine how ML normalizes for geology, spacin...

Is the Williston Basin running out of its best rock? In this episode of The Novi AI Roundup, we explore how machine learning is uncovering signs of productivity decay and what that means for future inventory. Drawing from the technical paper “Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion”, we examine how well performance...

How long does the impact of completions really last? In this episode of The Novi AI Roundup, we explore how machine learning and SHAP values are used to quantify the changing influence of completion design over the life of a well. Drawing from the technical paper “Quantifying the Diminishing Impact of Completions Over Time Across the Bakken, Eagle Ford, and Wolfcamp Using a Multi-Target Machine Le...

What if production data could reveal hidden geologic structures? In this episode of The Novi AI Roundup, we explore how machine learning uses production driver cross-sections to uncover regional geologic insights in the Bakken-Three Forks play. Drawing from the technical paper “Use of Machine Learning Production Driver Cross-Sections for Regional Geologic Insights in the Bakken-Three Forks Play”,...

Are unconventional well performance gains starting to slow down? In this episode of The Novi AI Roundup, we explore whether the steady improvements in shale productivity over the past decade are reaching their limits. Drawing from the URTeC 2021 paper “Are Unconventional Well Performance Gains Exhausted?”, we analyze how factors like longer laterals, larger completion designs, and development inte...

How can production forecasting scale across thousands of wells? In this episode of The Novi AI Roundup, we explore how autoregressive and machine learning models are transforming PDP forecasting in the Midland Basin. Drawing from the URTeC 2021 paper “Autoregressive and Machine Learning Driven Production Forecasting – Midland Basin Case Study”, we examine how automated ML workflows learn directly...

Is one spacing template enough for an entire development unit? In this episode of The Novi AI Roundup, we explore how machine learning can tailor pilot-proven spacing templates to individual development units for maximum acreage value. Drawing from the URTeC 2022 paper “Using Machine Learning to Customize Development Unit Spacing for Maximum Acreage Value”, we examine how optimized spacing strateg...

Why do some wells decline faster than others? In this episode of The Novi AI Roundup, we explore how machine learning helps engineers understand the physical drivers behind decline behavior, beyond just fitting curves. Drawing from the URTeC 2022 paper “Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values”, we examine how b values are shaped by well spaci...

How do you optimize field development with limited data? In this episode of The Novi AI Roundup, we explore how Shell and Novi Labs used machine learning to accelerate development planning in Argentina’s Neuquén Basin. Drawing from the URTeC 2022 paper “Accelerating Field Optimization for Shell in the Neuquén Basin using Novi Labs Machine Learning”, we break down how basin-specific models were bui...

Do longer laterals actually deliver more value? In this episode of The Novi AI Roundup, we explore how machine learning helps quantify the production efficiency of lateral length across U.S. unconventional plays. Drawing from the URTeC 2022 paper “The Diminishing Returns of Lateral Length Across Different Basins”, we show how uplift per foot declines beyond certain thresholds, and why production r...

What’s the cost of not knowing? In this episode of The Novi AI Roundup, we use a “time machine” method to test how much incremental subsurface data would have improved forecast accuracy, and how those changes translate into real-world capital impact. Drawing from the technical paper “How Much Better Could We Have Done?”, we explore the value of geologic features in machine learning models, where s...

Do completions still matter five years into a well’s life? In this episode of The Novi AI Roundup, we explore how the influence of completion design evolves over time, and how it varies across oil and gas plays. Drawing from the technical paper “How does the impact of completions change over the life of a well? A comparison across the major US unconventional plays using machine learning”, we discu...

How early is too early to trust a forecast? In this episode of The Novi AI Roundup, we explore how machine learning is helping engineers generate accurate PDP forecasts, even when only a few months of production data are available. Drawing from the technical paper “How Much Data is Needed to Create Accurate PDP Forecasts?”, we compare ML vs. traditional decline methods, highlight key sources of bi...

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, geome...

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 sh...

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 o...

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...

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...

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....

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,...

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 condi...

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 i...

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 G...