Permeability Prediction Using Machine Learning Methods for the CO<sub>2</sub> Injectivity of the Precipice Sandstone in Surat Basin, Australia
This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO<sub>...
Main Authors: | Reza Rezaee, Jamiu Ekundayo |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-03-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/15/6/2053 |
Similar Items
-
Experimental Investigation on the Effects of CO<sub>2</sub> Displacement Methods on Petrophysical Property Changes of Ultra-Low Permeability Sandstone Reservoirs Near Injection Wells
by: Qian Wang, et al.
Published: (2019-01-01) -
Application of Polymeric CO<sub>2</sub> Thickener Polymer-Viscosity-Enhance in Extraction of Low-Permeability Tight Sandstone
by: Hong Fu, et al.
Published: (2024-01-01) -
Applying NMR <i>T</i><sub>2</sub> Spectral Parameters in Pore Structure Evaluation—An Example from an Eocene Low-Permeability Sandstone Reservoir
by: Yan Lu, et al.
Published: (2021-08-01) -
The study of porosity and permeability characteristics of sandstone as reservoir rock /
by: Tan, Wai Loon, 1990-, author, et al.
Published: (2013) -
The study of porosity and permeability characteristics of sandstone as reservoir rock /
by: Tan, Wai Loon, 1990-, author, et al.
Published: (2013)