Identification of Lacustrine Shale Lithofacies from FMI Image and ECS Log Using Machine Learning with GLCM Features

Author:

Tian Min,Tan Maojin,Wang Min

Publisher

Elsevier BV

Reference42 articles.

1. Performance evaluation of machine learningbased classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia;Gian Antariksa;Journal of Petroleum Science and Engineering,2022

2. Permeability determination of cores based on their apparent attributes in the Persian Gulf region using Navie Bayesian and Random Forest algorithms;P Behnoud Far;Journal of Natural Gas Science and Engineering,2017

3. Integrated data-driven 3D shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota, United States;Shuvajit Bhattacharya;Journal of Petroleum Science & Engineering,2019

4. Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA;Carr Bhattacharya;Journal of Natural Gas and Engineering,2016

5. Automatic identification of sedimentary facies and lithology of reef flat reservoir from FMI logging;Chai Hua;Applied Geophysics,2009

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