Are unconventional well performance gains exhausted? Investigating the drivers of year-over-year production improvements across the major US unconventional plays using machine learning Talk ...
[URTeC 2021 Paper] Use of Machine Learning Production Driver Cross-Sections for Regional Geologic Insights in the Bakken-Three Forks Play
Machine learning explainability tools like SHAP values provide a powerful tool to understand how geologic performance drivers vary across a play. Talk Details:: - ...
[URTeC 2021 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
In this paper we use our models to evaluate how the impact of completions varies over the life of a well, across ...
[URTeC 2021 Paper] Machine Learning Methods in the Williston: A Case Study in Productivity Decay and the Implications for Inventory Exhaustion
What does remaining inventory look like in the Bakken? This study examines how rock quality of remaining inventory varies around the basin, and ...
[URTeC 2021] Autoregressive and Machine Learning Driven Production Forecasting – Midland Basin Case Study
This paper details the results of Pioneer’s pilot study with Novi on automated PDP forecasting, and their vision for a fully-automated, ML-based ...