A Physics-informed Machine Learning Workflow to Forecast Production in a Fractured Marcellus Shale Reservoir-Reference-Cited by-同舟云学术

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A Physics-informed Machine Learning Workflow to Forecast Production in a Fractured Marcellus Shale Reservoir

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th Unconventional Resources Technology Conference
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Author:

Gross Michael R.,Hyman Jeffrey D.,Srinivasan Shriram,O’Malley Daniel,Karra Satish,Mudunuru Maruti K.,Sweeney Matthew,Frash Luke,Carey Bill,Guthrie George D.,Carr Tim,Li Liwei,Crandall Dustin,Viswanathan Hari

Publisher

American Association of Petroleum Geologists

Cited by 5 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. Physics‐Informed Gas Lifting Oil Well Modelling using Neural Ordinary Differential Equations;INCOSE International Symposium;2023-07

2. Optimization Model of Fracture Parameters of Shale Gas Fracturing Horizontal Well Based on Genetic Algorithm;Day 2 Mon, February 20, 2023;2023-03-07

3. High-quality fracture network mapping using high frequency logging while drilling (LWD) data: MSEEL case study;Machine Learning with Applications;2022-12

4. Design and Implementation of Field Tests in Unconventional Reservoirs: Practical Perspectives;Day 1 Mon, October 03, 2022;2022-09-26

5. Numerical Simulation via CFD Methods of Nitrogen Flooding in Carbonate Fractured-Vuggy Reservoirs;Energies;2021-11-12

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