Research on the Application of Integrated Learning Models in Oilfield Production Forecasting-Reference-Cited by-同舟云学术

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Research on the Application of Integrated Learning Models in Oilfield Production Forecasting

Published:2023-10-10 Issue:42 Volume:8 Page:39583-39595
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Ni MingCheng¹^ORCID,Xin XianKang¹²³^ORCID,Yu GaoMing¹²³,Liu Yu¹,Gong YuGang¹

Affiliation:

1. School of Petroleum Engineering, Yangtze University, Wuhan, Hubei 430100, China

2. Hubei Provincial Key Laboratory of Oil and Gas Drilling and Production Engineering (Yangtze University), Wuhan, Hubei 430100, China

3. School of Petroleum Engineering, Yangtze University: National Engineering Research Center for Oil and Gas Drilling and Completion Technology, Wuhan, Hubei 430100, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c05422

Reference32 articles.

1. Oil shocks and global economy

2. Data-driven deep-learning forecasting for oil production and pressure

3. Ling, K.; He, J. Theoretical Bases of Arps Empirical Decline Curves, in: All Days. Presented at the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, UAE; Society of Petroleum Engineers, 2012; p 161767.

4. A New Model for Modern Production-Decline Analysis of Gas/Condensate Reservoirs

5. Production Decline Curves of Tight Oil Wells in Eagle Ford Shale

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