Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity-Reference-Cited by-同舟云学术

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Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity

Published:2020-11-20 Issue:6 Volume:14 Page:536-555
ISSN:1932-1864
Container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal
language:en
Short-container-title:Stat Anal Data Min: The ASA Data Sci Journal

Author:

Attanasi Emil D.¹^ORCID,Freeman Philip A.¹,Coburn Timothy C.²

Affiliation:

1. Geology, Energy and Minerals Science Center, U.S. Geological Survey Reston Virginia USA

2. School of Energy Economics, Policy and Commerce, University of Tulsa Tulsa Oklahoma USA

Funder

U.S. Geological Survey

Publisher

Wiley

Subject

Computer Science Applications,Information Systems,Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11487

Reference36 articles.

1. Permutation importance: a corrected feature importance measure

2. J. D.Baihlyet al. Has the economic stage count been reached in the Bakken Shale?Proceedings of the 2012 SPE hydrocarbon economics and evaluation symposium (Calgary Alberta 2012) SPE Richardson Texas 2012 p. 23.https://doi.org/10.2118/159683‐MS

3. Growth Drivers of Bakken Oil Well Productivity

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Price Responsiveness of Shale Oil: A Bakken Case Study;Natural Resources Research;2022-01-24

2. Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry;Natural Resources Research;2021-09-30

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