Integrated AutoML-based framework for optimizing shale gas production: A case study of the Fuling shale gas field
This study introduces a comprehensive and automated framework that leverages data-driven methodologies to address various challenges in shale gas development and production. Specifically, it harnesses the power of Automated Machine Learning (AutoML) to construct an ensemble model to predict the esti...
Saved in:
Main Authors: | Tianrui Ye, Jin Meng, Yitian Xiao, Yaqiu Lu, Aiwei Zheng, Bang Liang |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co., Ltd.
2025-03-01
|
Series: | Energy Geoscience |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759224000805 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Study on Hydraulic Fracture Propagation of Strong Heterogeneous Shale Based on Stress‐Seepage Damage Coupling Model
by: Wei Liu, et al.
Published: (2025-01-01) -
Formulation and characterization of surfactants with antibacterial and corrosion-inhibiting properties for enhancing shale gas drainage and production
by: Jia Li, et al.
Published: (2025-01-01) -
Influencing factors and quantitative prediction of gas content of deep marine shale in Luzhou block
by: Xinyang He, et al.
Published: (2025-01-01) -
Determination of the Hydration Damage Instability Period in a Shale Borehole Wall and Its Application to a Fuling Shale Gas Reservoir in China
by: Haicheng She, et al.
Published: (2019-01-01) -
Impact of Geological Factors on Marine Shale Gas Enrichment and Reserve Estimation: A Case Study of Jiaoshiba Area in Fuling Gas Field
by: Siyu Yu, et al.
Published: (2021-01-01)