Data-Driven Approach for the Prediction of In Situ Gas Content of Deep Coalbed Methane Reservoirs Using Machine Learning: Insights from Well Logging Data
Saved in:
Main Authors: | Qian Zhang, Shuheng Tang, Songhang Zhang, Zhaodong Xi, Tengfei Jia, Xiongxiong Yang, Donglin Lin, Wenfu Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
American Chemical Society
2025-01-01
|
Series: | ACS Omega |
Online Access: | https://doi.org/10.1021/acsomega.4c08679 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Geological characteristics and development effect evaluation of coalbed methane reservoirs in Panguan syncline, Guizhou Province
by: Haiyang HU, et al.
Published: (2025-01-01) -
Research on the molecular dynamics of coalbed methane diffusion and adsorption in reservoir pores under different factors
by: Xuefan Wang, et al.
Published: (2025-01-01) -
Division and Effect Evaluation of Fracking Outburst Elimination Zones in Surface Extraction Wells of Coalbed Methane
by: Jianbao Liu, et al.
Published: (2024-01-01) -
Pattern Recognition of the Vertical Hydraulic Fracture Shapes in Coalbed Methane Reservoirs Based on Hierarchical Bi-LSTM Network
by: Zhaozhong Yang, et al.
Published: (2020-01-01) -
Research on the Construction of Coal Powder Settling Final Velocity Model for Coalbed Methane Wells in Panhe Block
by: Zhou Zhang, et al.
Published: (2025-01-01)