Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks

Abstract The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heter...

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發表在:	Geomechanics and Geophysics for Geo-Energy and Geo-Resources
Main Authors:	Umar Ashraf, Aqsa Anees, Hucai Zhang, Muhammad Ali, Hung Vo Thanh, Yujie Yuan
格式:	Article
語言:	英语
出版:	Springer 2024-08-01
主題:	Unsupervised ML algorithms SOM K-means clustering Payable clusters distribution DBSCAN SHAP
在線閱讀:	https://doi.org/10.1007/s40948-024-00848-9