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 from production history, improve forecast consistency, and enable faster decision-making across large asset portfolios.

This podcast episode is based on the technical paper “Autoregressive and Machine Learning Driven Production Forecasting – Midland Basin Case Study”, authors: I. Gupta, O. Samandarli, A. Burks, D. McMaster, V. Jayaram, D. Niederhut, T. Cross. Download the full paper here.

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 strategies account for geology, interference, and local performance trends, and why customized development can outperform standardized designs.This podcast episode is based on the technical paper “Using Machine Learning to Customize Development Unit Spacing for Maximum Acreage Value”, authors: M. Maguire, T. Witham, A. Cui, T. Cross. The paper was presented at URTeC 2022. Download the full paper here.

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 spacing, completion design, and reservoir quality, and how this insight improves forecast confidence and long-term planning.This podcast episode is based on the technical paper “Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values”, authors: D. Niederhut, A. Cui, C. Macalla, J. Reed. The paper was presented at URTeC 2022. Download the full paper here.

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 built with sparse data, what drove uplift in Vaca Muerta, and how operators can apply ML to make faster, more confident decisions.

This podcast episode is based on the technical paper “Accelerating Field Optimization for Shell in the Neuquén Basin using Novi Labs Machine Learning”, presented at URTeC 2022 in collaboration with Shell. Authors: DP. Zannitto, C. Kosa. Download the full paper here.

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 results vary by basin, completions size, and spacing. From the Midland to the Williston, this episode challenges the assumption that longer is always better.This podcast episode is based on the technical paper “The Diminishing Returns of Lateral Length Across Different Basins”, authors: A. Cui, T. Cross. Download the full paper here.

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 small insights drive big outcomes, and how engineering teams can quantify the upside of better data.This podcast episode is based on the technical paper “How Much Better Could We Have Done? Using a Time Machine Method to Quantify the Impact of Incremental Geologic Data on Machine Learning Forecast Accuracy”, authors: J. Reed, C. Macalla. Download the full paper here.

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 discuss where completions drive uplift, when geology takes over, and what this means for long-term forecasting and development strategy.This podcast episode is based on 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”, authors: T. Cross, K. Long, D. Niederhut, A. Cui. Download the full paper here.

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 bias, and share what engineers need to know before forecasting wells with limited history.

This podcast episode is based on the technical paper “How Much Data is Needed to Create Accurate PDP Forecasts?”, authors: Austin Lim, Alexander Cui, Ted Cross. Download the full paper here.

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.