Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs

Journals Menu

Follow SCIRP

ISSN Print: 2153-0653
ISSN Online: 2153-0661
www.scirp.org/journal/ica
E-mail: ica@scirp.org

"Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs"
written by Haleh Azizi, Hassan Reza,
published by Intelligent Control and Automation, Vol.12 No.2, 2021

has been cited by the following article(s):

[1]	Natural fractures characterization by integration of FMI logs, well logs and core data: a case study from the Sarvak Formation (Iran)
	Journal of Petroleum …, 2023

[2]	Deteksjon av bergsprekker ved hjelp av lidar: en studie av intensitet
	2022

[1]	Identification of natural fractures in shale gas reservoirs using fracture signature function and machine learning models Unconventional Resources, 2024 DOI:10.1016/j.uncres.2023.100069

[2]	Natural fractures characterization by integration of FMI logs, well logs and core data: a case study from the Sarvak Formation (Iran) Journal of Petroleum Exploration and Production Technology, 2023 DOI:10.1007/s13202-023-01611-8

[3]	Natural fractures characterization by integration of FMI logs, well logs and core data: a case study from the Sarvak Formation (Iran) Journal of Petroleum Exploration and Production Technology, 2023 DOI:10.1007/s13202-023-01611-8

Follow SCIRP