Fracture Conductivity Prediction Based on Machine Learning-Reference-Cited by-同舟云学术

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Fracture Conductivity Prediction Based on Machine Learning

Published:2024-03-08 Issue: Volume: Page:
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Wang Xiaopeng¹²,Zhang Binqi¹²,Du Jianbo¹²,Liu Dongdong¹²,Zhang Qilong¹²,Liu Xiaoqiang³⁴^ORCID

Affiliation:

1. State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China

2. Tianjin Branch of CNOOC Ltd., Tianjin 300450, China

3. School of Energy Resources, China University of Geosciences Beijing, Beijing 100083, China

4. School of Earth and Spacing Sciences, Peking University, Beijing 100871, China

Funder

China National Petroleum Corporation

National Natural Science Foundation of China

State and Key Laboratory of Offshore Oil Exploitation

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.4c00448

Reference36 articles.

1. Understanding hydraulic fracture mechanisms: From the laboratory to numerical modelling

2. An innovative technology of directional propagation of hydraulic fracture guided by radial holes in fossil hydrogen energy development

3. Proppants- What 30 Years of Study has Taught Us

4. Hydraulic fracturing-induced seismicity characterization through coupled modeling of stress and fracture-fault systems

5. Hydraulic fracture initiation theory for a horizontal well in a coal seam

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