relation: https://khub.utp.edu.my/scholars/16840/
title: Application of Machine Learning for Shale Reservoir Permeability Prediction
creator: Prajapati, S.
creator: Padmanabhan, E.
description: 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. © Published under licence by IOP Publishing Ltd.
publisher: Institute of Physics
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Prajapati, S. and Padmanabhan, E.  (2022) Application of Machine Learning for Shale Reservoir Permeability Prediction.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129841723&doi=10.1088%2f1755-1315%2f1003%2f1%2f012025&partnerID=40&md5=51d19f5650e5763ccfd99b1b9301e454
relation: 10.1088/1755-1315/1003/1/012025
identifier: 10.1088/1755-1315/1003/1/012025