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An eigenvalue-driven framework for the ranking and selection of optimal geological CO2 storage sites

固有値駆動型フレームワークによる最適な地中CO2貯留サイトのランク付けと選定 (AI 翻訳)

iranfar, soha, Sadeghpour, Farshad, Khaksar Manshad, Abbas, Naderi, Meysam, Shakiba, Mahmood

Zenodoプレプリント2026-07-21#CCUS経営インパクト: コスト削減対象セクター: oil_gas
DOI: 10.1016/j.rineng.2025.106770
原典: https://zenodo.org/records/21474810
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🤖 gxceed AI 要約

日本語

地中CO2貯留は気候変動対策の鍵であり、本研究は固有値行列を用いたMCDM手法により、世界各地の60サイトの10パラメータを分析し、最適な貯留サイトを選定するフレームワークを提案。汚染指数が最も重要な因子であり、サイトを高・中・低ポテンシャルに分類した。

English

This study proposes an eigenvalue matrix-based MCDM framework to rank and select optimal geological CO2 storage sites. Analyzing 10 parameters from 60 global sites, it identifies pollution per capita as the most influential factor and classifies sites into high, medium, and low potential, aiding CCS decision-making.

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

日本は2030年までにCO2貯留量120万トンを目標としており、本手法は国内の地層評価や候補地選定に応用可能。規制当局や事業者が実用的な比較ツールとして活用できる。

In the global GX context

CCS is central to global decarbonization, and this eigenvalue-driven framework offers a systematic, transparent method for site selection, applicable in regions like the North Sea or US Gulf Coast. It supports regulators and industry in prioritizing storage investments.

👥 読者別の含意

🔬研究者:Provides a novel MCDM approach integrating reservoir, petrophysical, and geomechanical parameters for CCS site selection.

🏢実務担当者:Use the scoring system to rank potential CO2 storage sites and justify investment decisions.

🏛政策担当者:Adopt this framework to establish transparent site classification guidelines for CCS project approvals.

📄 Abstract（原文）

Underground CO2 storage is vital to carbon capture and storage (CCS) strategies designed to combat climate change by lowering atmospheric CO2 levels. This method involves capturing CO2 emissions from industrial sources and storing them in geological formations, such as depleted oil and gas reservoirs and saline aquifers. New techniques, such as Multi-Criteria Decision Making (MCDM), machine learning, and mathematical methods, are increasingly recognized as practical approaches for comparing and selecting suitable sites for underground CO2 storage. This study employs the mathematical method of the eigenvalue matrix to identify the best locations for CO2 storage. To accomplish this, 10 parameters related to reservoir characteristics, petrophysical properties, and geomechanical features of 60 sites worldwide were analyzed. The influence coefficients for each of the 10 parameters were determined using the eigenvalue matrix. Among these parameters, pollution per capita had the highest coefficient at 0.259, while pressure had the lowest coefficient at 0.029. Each of the 60 investigated sites was assigned a score based on the coefficients assigned to various parameters. These sites were then classified into three categories: high potential, medium potential, and low potential. The findings of this study can assist in selecting the most suitable site for underground CO2 storage across different locations worldwide.

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