Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models

Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models

Abstract Lithofacies identification plays a pivotal role in understanding reservoir heterogeneity and optimizing production in tight sandstone reservoirs. In this study, we propose a novel supervised workflow aimed at accurately predicting lithofacies in complex and heterogeneous reservoirs with int...

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Bibliographic Details
Main Authors: Muhammad Ali, Peimin Zhu, Ren Jiang, Ma Huolin, Umar Ashraf, Hao Zhang, Wakeel Hussain
Format: Article
Language:English
Published: Springer 2024-04-01
Series:Geomechanics and Geophysics for Geo-Energy and Geo-Resources
Subjects:
Facies classification
Machine learning
Well log
Two-information criteria clustering technique
Online Access:https://doi.org/10.1007/s40948-024-00787-5

Internet

https://doi.org/10.1007/s40948-024-00787-5

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