Novi Technical Papers

Novi Technical Papers are written and peer-reviewed by experts in oil & gas, machine learning, and data science. Browse our technical papers on the latest advances in oil & gas focused machine learning and data Science

  • All
  • AAPG 2020
  • URTeC 2020
  • URTeC 2021
  • URTeC 2022
All
  • All
  • AAPG 2020
  • URTeC 2020
  • URTeC 2021
  • URTeC 2022
Technical Papers

[URTeC 2022] The Diminishing Returns of Lateral Length Across Different Basins (ID 3723784)

The Diminishing Returns of Lateral Length Across Different Basins Talk Details:: – Tuesday, June 21st at 2:15 PM | Room 371 – Theme 7: Applications …

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Technical Papers

[URTeC 2022] Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning

Accelerating field optimization for Shell in the Neuquén Basin using Novi Labs machine learning AUTHORS DP. Zannitto2, C. Kosa1 (1. Novi Labs 2. Shell Compania …

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Technical Papers

[URTeC 2022] Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values (ID 3723711)

Understanding the Spacing, Completions, and Geological Influences on Decline Rates and B Values Talk Details:: – Wednesday, July 28th at 11:15 AM | Room 371 …

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Technical Papers

[URTeC 2022] Using Machine Learning to Customize Development Unit Spacing for Maximum Acreage Value (ID 3723023)

Using Machine Learning to Customize Development Unit Spacing for Maximum Acreage Value Talk Details:: – Wednesday, July 22nd at 8:55 AM | Room 360 – …

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Technical Papers

[URTeC 2022] How does the impact of completions change over the life of a well? A comparison across the major US unconventional plays using machine learning (ID 3723930)

How does the impact of completions change over the life of a well? A comparison across the major US unconventional plays using machine learning Talk …

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Technical Papers

[URTeC 2022] 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 (ID 3723907)

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 …

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