Simultaneous well spacing and completion optimization using an automated machine learning approach. A case study of the Marcellus Shale reservoir, northeastern United States
Author:
Affiliation:
1. Department of Petroleum and natural gas engineering, West Virginia University, Morgantown, West Virginia 26506, USA
2. Independent Researcher, Seattle, WA 98109, USA
3. Obsertelligence LLC, Aubrey, Texas 76227-5741, USA
Abstract
Publisher
Geological Society of London
Subject
Earth and Planetary Sciences (miscellaneous),Economic Geology,Geochemistry and Petrology,Geology,Fuel Technology
Link
https://www.lyellcollection.org/doi/pdf/10.1144/petgeo2023-077
Reference39 articles.
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2. Shale Gas-in-Place Calculations Part I: New Pore-Scale Considerations
3. Ansari A. Fathi E. Belyadi F. Takbiri-Borujeni A. and Belyadi H. 2018. Data-based smart model for real-time liquid loading diagnostics in Marcellus Shale via machine learning. SPE Canada Unconventional Resources Conference Calgary Alberta Canada March 2018 SPE-189808-MS https://doi.org/10.2118/189808-MS
4. Genetic Programming
5. Drawdown key to well performance;Belyadi H.;The American Oil & Gas Reporter,2017
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