← 論文一覧に戻る

Experimental Investigation of H2-Brine Relative Permeability in Tight Sandstone for Assessing Underground Hydrogen Storage Efficiency

水素地下貯蔵効率評価のためのタイト砂岩におけるH2-ブライン相対浸透率の実験的調査 (AI 翻訳)

Jenei, Bettina, Al-Masri, Wael, Youssef, Souhail, Alwardy, Basil, Hagemann, Birger, Ganzer, Leonhard

Zenodoプレプリント2026-08-03#水素Origin: EU対象セクター: energy
DOI: 10.5281/zenodo.21777308
原典: https://zenodo.org/records/21777308
📄 PDF

🤖 gxceed AI 要約

日本語

この研究は、枯渇したガス貯留層を利用した水素地下貯蔵(UHS)の効率評価に必要な、タイト砂岩における水素-ブライン相対浸透率の実験的測定を実施した。水素の特性により、CO2やN2のアナログデータと異なる浸透率挙動を示し、既存データの直接適用は予測誤差を生むことを明らかにした。流体・浸透率固有のパラメータ設定の重要性を強調している。

English

This study experimentally measures H2-brine relative permeability in tight sandstone for underground hydrogen storage (UHS) efficiency assessment. Results show significant differences from CO2/N2 analogue data due to hydrogen's unique properties, indicating that direct transfer of analogue data leads to predictive errors. Emphasizes the need for fluid- and permeability-specific parameterization for reliable UHS modeling.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では、水素社会実現に向けた国家戦略が進められており、地下貯蔵は再生可能エネルギーの変動を吸収する重要な技術として注目されている。本研究は、日本の水素サプライチェーン構築における貯蔵インフラ設計に直接貢献する知見を提供する。

In the global GX context

Globally, underground hydrogen storage is critical for integrating intermittent renewables and achieving decarbonization targets. This study provides essential experimental data for UHS simulation, addressing a key gap in hydrogen storage research and supporting the development of reliable large-scale energy storage solutions.

👥 読者別の含意

🔬研究者:Provides novel experimental data on H2-brine relative permeability in tight sandstone, essential for improving UHS simulation models.

🏢実務担当者:Informs engineering decisions for UHS site selection and operational design by highlighting the need for site-specific relative permeability data.

🏛政策担当者:Supports policy development for hydrogen storage infrastructure by providing evidence on technical feasibility and data requirements.

📄 Abstract(原文)

Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs and other porous formations such as aquifers is increasingly considered a key large-scale energy buffer for supporting intermittent renewable energy systems. Reliable prediction of hydrogen injectivity, migration, trapping, and recovery efficiency requires robust multiphase flow characterisation, particularly H₂ brine relative permeability. However, hydrogen's low density, high diffusivity, and distinct interfacial properties introduce displacement behaviours that differ fundamentally from those of CO₂ or N₂ based analogues. Experimental datasets for H₂ brine systems, especially in tight sandstones, remain scarce due to operational complexity, hydrogen safety constraints, and measurement uncertainties under low permeability conditions. This study presents primary drainage H₂ brine relative permeability measurements on tight sandstone core plugs spanning a permeability range representative of aquifers originated from depleted gas reservoir intervals considered for UHS. Unsteady state core flooding experiments were conducted in a laboratory approved for hydrogen use equipped with detection, ventilation, and safety control systems, with relative permeability curves derived from measured flow rates and brine production using history matching. The resulting functions exhibit pronounced permeability dependence and systematic differences relative to published CO₂-brine and N₂-brine analogue datasets attributable to hydrogen's distinct fluid properties. These findings demonstrate that directly transferring analogue gas brine relative permeability data to UHS simulations may lead to significant predictive errors in injectivity, pressure evolution, and working gas capacity, underscoring the necessity of fluid and permeability specific parameterisation for reliable UHS performance modelling. 

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。

gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。