EP 30 · Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion - The Novi AI Roundup | Transcription & Insights

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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.

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The Novi AI Roundup

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

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