The Novi AI Roundup

Novi Labs
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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.

  1. 4天前

    EP 33 · Predicting Water Production in the Williston Basin using a Machine Learning Model

    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 water production, helping operators better plan infrastructure, manage disposal costs, and optimize field development. This podcast episode is based on the technical paper “Predicting Water Production in the Williston Basin using a Machine Learning Model”, authors: D. Niederhut, K. Crifasi, K. Darnell, K. Sathaye, T. Cross. Download the full paper here.

    22 分钟

  2. 6月25日

    EP 32 · The Impact of Interwell Spacing Over Time: A Machine Learning Approach

    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 interference throughout a well’s life, and why short-term production gains can sometimes come at the expense of long-term value. This podcast episode is based on the technical paper “The Impact of Interwell Spacing Over Time: A Machine Learning Approach”, authors: D. Niederhut, J. Ramey, J. Reed, K. Darnell, K. Sathaye, T. Cross. Download the full paper here.

    20 分钟

  3. 5月14日

    EP 31 · Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model

    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, spacing, and development conditions to uncover the true drivers of well performance, and why apples-to-apples benchmarking matters in mature unconventional plays.This podcast episode is based on the technical paper “Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model”, authors: D. Niederhut, K. Crifasi, K. Darnell, K. Sathaye, T. Cross. Download the full paper here.

    20 分钟

  4. 5月7日

    EP 30 · Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion

    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 evolves as development expands, and how ML helps identify shifts in location quality that traditional analysis can miss.This podcast episode is based on the technical paper “Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion”, authors: B. L. Myers, B. Davis, R. Duman, T. Cross. Download the full paper here.

    13 分钟

  5. 4月30日

    EP 29 · Quantifying the Diminishing Impact of Completions Over Time Across the Bakken, Eagle Ford, and Wolfcamp Using a Multi-Target Machine Learning Model and SHAP Values

    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 Learning Model and SHAP Values”, we examine how completion-driven uplift peaks early, fades over time, and gives way to geological and reservoir-driven performance across major U.S. plays.This podcast episode is based on the technical paper “Quantifying the Diminishing Impact of Completions Over Time Across the Bakken, Eagle Ford, and Wolfcamp Using a Multi-Target Machine Learning Model and SHAP Values”, authors: T. Cross, D. Niederhut, A. Cui, K. Sathaye, J. Chaplin. Download the full paper here.

    23 分钟

  6. 3月26日

    EP 28 · Use of Machine Learning Production Driver Cross-Sections for Regional Geologic Insights in the Bakken-Three Forks Play

    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”, we examine how subsurface variability impacts well performance, and how these insights can guide better targeting and development decisions in a mature basin.This podcast episode is based on the technical paper “Use of Machine Learning Production Driver Cross-Sections for Regional Geologic Insights in the Bakken-Three Forks Play”. Authors: T. Cross, K. Sathaye, J. Chaplin. Download the full paper here: https://novilabs.com/resources/urtec-2021-machine-learning-bakken-production-drivers/

    24 分钟

  7. 3月19日

    EP 27 · Are Unconventional Well Performance Gains Exhausted?

    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 intensity have driven year-over-year production improvements, and what machine learning reveals about the future trajectory of well performance across major U.S. unconventional plays.This podcast episode is based on the technical paper “Are Unconventional Well Performance Gains Exhausted?”, authors: T. Cross, J. Chaplin, K. Sathaye, A. Cui. Download the full paper here.

    19 分钟

  8. 3月5日

    EP 26 · Autoregressive and Machine Learning Driven Production Forecasting – Midland Basin Case Study

    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.

    23 分钟
查看全部 34 集

关于

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.

信息

  • 创作者
    Novi Labs
  • 活跃年份
    2025年 - 2026年
  • 单集
    34
  • 分级
    儿童适宜
  • 版权
    © Novi Labs
  • 节目网站
    The Novi AI Roundup

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