Open AccessApplication of Machine Learning for Shale Reservoir Permeability Prediction
Author(s) -
Srichand Prajapati,
Eswaran Padmanabhan
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/1003/1/012025
Subject(s) - oil shale , permeability (electromagnetism) , petroleum engineering , reservoir modeling , geology , chemistry , paleontology , biochemistry , membrane
Due to ultra-low permeability, the characterization of shale reservoir is always being a challenge. The traditional models are insufficient to estimate the ultra-low permeability of shale reservoirs. Based on Machine Learning, we proposed a simple mathematical approach to predict the permeability of shale reservoirs. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction because of their strengths to deliver the variables. Additionally, used the Kozeny’s equation with power mean approach to constraint the estimated permeability for more reliable. To do this, we used a pure shale well-log downloaded from open source. The results show that the predicted permeability is well correlated with the neutron log and significantly match with the other well-logs.