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EP 21 · 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
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.
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By Novi LabsWhat’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.