Robust development of data-driven models for methane and hydrogen mixture solubility in brine

Robust development of data-driven models for methane and hydrogen mixture solubility in brine

Abstract Within the domain of hydrogen storage initiatives inside subterranean structures, the accurate estimation of solubility of methane and hydrogen mixtures in brine becomes vital. In this paper, we aim to form robust data-driven intelligent algorithms founded on various machine learning method...

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Bibliographic Details
Main Authors: Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Geomechanics and Geophysics for Geo-Energy and Geo-Resources
Subjects:
Hydrogen energy
Hydrogen/methane solubility
Outlier detection
Sensitivity study
Machine learning
Intelligent modeling
Online Access:https://doi.org/10.1007/s40948-025-00947-1
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https://doi.org/10.1007/s40948-025-00947-1

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