Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

Abstract Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop int...

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Bibliographic Details
Main Authors: Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Saturation exponent
Data-driven intelligent modeling
Sensitivity analysis
Outlier detection
Online Access:https://doi.org/10.1038/s41598-024-84556-0
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