Machine Learning–Based Estimation of Hydrogen Solubility in Brine for Underground Storage in Saline Aquifers

Fahd Mohamad Alqahtani, Menad Nait Amar, Hakim Djema, Khaled Ourabah, Amer Alanazi and Mohammad Ghasemi

Greenhouse Gases: Science and Technology, 2025, vol. 15, issue 3, 409-420

Abstract: Saline aquifers are considered among the most attractive porous media systems for underground hydrogen storage (UHS) because of their wide availability and the considerable capacity of storage. The successful implementation of UHS in saline aquifers depends on many vital factors and parameters. Among these factors, the solubility of hydrogen (H2) in brine remains a relevant consideration, particularly due to its influence on potential bio‐geochemical reactions that may occur within underground formations. Given the significant expense and time demands associated with experimental methods for determining hydrogen solubility in brine, there is a growing need for a reliable and low‐cost alternative capable of delivering accurate predictions. In this research, a suite of robust machine learning (ML) schemes, including multilayer perceptron (MLP), genetic programming (GP), and the group method of data handling (GMDH), is employed to construct predictive models for hydrogen solubility in brine, specifically under challenging high‐pressure and high‐temperature scenarios. The obtained results demonstrated the promising performance of the newly suggested ML‐based paradigms. MLP optimized with Levenberg–Marquardt (MLP‐LMA) yielded the best statistical metrics, including an R2 of 0.9991 and an average absolute relative error (AARE) of 0.9417%. The findings of this study are important because they demonstrate that ML‐based approaches embodied in intelligent paradigms are accurate and efficient and therefore have potential for use in reservoir simulators to assess dissolution processes associated with UHS in porous media.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/ghg.2353

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:greenh:v:15:y:2025:i:3:p:409-420

Access Statistics for this article

More articles in Greenhouse Gases: Science and Technology from Blackwell Publishing
Bibliographic data for series maintained by Wiley Content Delivery ().