Revolutionizing Reservoir Management with Machine Learning: Enhancing Efficiency and Decision-Making in the Oil and Gas Industry-Reference-Cited by-同舟云学术

Revolutionizing Reservoir Management with Machine Learning: Enhancing Efficiency and Decision-Making in the Oil and Gas Industry

Published:2023-11-14 Issue: Volume:18 Page:293-299
ISSN:
Container-title:2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD)
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Author:

Chandramauli Awadhesh¹,Sapaev B.²,Alzubaidi Laith H.³,Pathak Shriram⁴,Rai Anjani Kumar⁵,Vennila C.⁶,Mouli Kathi Chandra⁷

Affiliation:

1. Uttaranchal University,Uttaranchal Institute of Technology,Department of Civil Engineering,Dehradun,India,248007

2. Alfraganus University,Faculty of Medicine,Department of Pharmaceuticals and Chemistry,Tashkent,Uzbekistan

3. The Islamic University,Najaf,Iraq

4. IES University,IES Institute of Technology and Management,Department of Mechanical Engineering,Bhopal,Madhya Pradesh,India,462044

5. GLA University,Department of CEA,MATHURA,India

6. Prince Shri Venkateshwara Padmavathy Engineering College,S&H,Chennai,India,127

7. GRIET,Hyderabad,Telangana,India

Publisher

IEEE

Link

Reference12 articles.

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2. Reduced-order modeling for oil-water and compositional systems, with application to data assimilation and production optimization;He,2013

3. Parallel reservoir simulation using a specific software framework (SPE 163653);Maliassov

5. A new method to predict production of oilfields in ultrahigh water-cut stage [J];Zhong;Fault-Block Oil and Gas Field,2011