Artificial Intelligence Applications in Petroleum Exploration and Production-Reference-Cited by-同舟云学术

Artificial Intelligence Applications in Petroleum Exploration and Production

Published:2023-05-19 Issue:10 Volume:13 Page:6214
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Li Hangyu¹²,Song Xianzhi³,Liu Shuyang¹²^ORCID

Affiliation:

1. Key Laboratory of Unconventional Oil & Gas Development of Ministry of Education, China University of Petroleum (East China), Qingdao 266580, China

2. School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China

3. College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Abstract

Recent advances in computer and data sciences have made artificial intelligence techniques a useful tool in tackling the problems in petroleum exploration and production [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/10/6214/pdf

Reference11 articles.

1. Li, Q., Wang, J., and Yin, H. (2023). Intelligent stuck pipe type recognition using digital twins and knowledge graph model. Appl. Sci., 13.

2. Zang, C., Lu, Z., Ye, S., Xu, X., Xi, C., Song, X., Guo, Y., and Pan, T. (2022). Drilling parameters optimization for horizontal wells based on a multiobjective genetic algorithm to improve the rate of penetration and reduce drill string drag. Appl. Sci., 12.

3. Fang, C., Wang, Z., Song, X., Zhu, Z., Yang, D., and Liu, M. (2022). A novel cementing quality evaluation method based on convolutional neural network. Appl. Sci., 12.

4. Li, H., Gong, C., Liu, S., Xu, J., and Imani, G. (2022). Machine learning-assisted prediction of oil production and CO2 storage effect in CO2-water-alternating-gas injection (CO2-WAG). Appl. Sci., 12.

5. Yin, H., Si, M., Cui, H., Li, Q., and Liu, W. (2022). Combining knowledge and a data driven method for identifying the gas kick type in a fractured formation. Appl. Sci., 12.