Machine Learning Applications in CO2 Geological Sequestration: A Review of Pre-Injection Evaluation, Injection Optimization, and Post-Injection Monitoring
CO2地中貯留における機械学習応用：注入前評価、注入最適化、注入後モニタリングに関するレビュー (AI 翻訳)
Watheq J. Al‐Mudhafar, Ahmed Alsubaih, Kamy Sepehrnoori
🤖 gxceed AI 要約
日本語
本レビューは、CO2地中貯留（CCS）の各段階（注入前評価、注入最適化、注入後モニタリング）における機械学習（ML）の応用を体系的に調査。ランダムフォレスト、SVR、XGBoostなどの従来手法に加え、深層学習による異常検知や不確実性定量化を紹介。Sleipnerなど実証事例を通じ、MLがコスト削減、安全性向上、予測精度向上に貢献することを示す。最後にデータ不足や解釈可能性などの課題を指摘し、統合MLフレームワークを提案。
English
This review systematically examines ML applications in the CCS lifecycle, covering pre-injection evaluation, injection optimization, and post-injection monitoring. It covers methods like Random Forest, SVR, XGBoost, and deep learning for anomaly detection and uncertainty quantification. Case studies (Sleipner, Illinois Basin-Decatur, etc.) demonstrate ML's role in cost reduction, safety, and predictive accuracy. Challenges such as data scarcity and interpretability are discussed, and a unified ML framework is proposed.
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CCSを重要な脱炭素手段と位置づけているが、地質条件やコスト面で課題がある。本レビューはMLによるコスト削減やリスク低減の可能性を示しており、日本のCCSプロジェクト（苫小牧など）への応用や、規制・政策策定の参考となる。
In the global GX context
CCS is a key global decarbonization strategy, and ML can significantly improve its efficiency and safety. This comprehensive review benchmarks ML integration across the CCS lifecycle, offering insights for project developers, regulators, and researchers worldwide.
👥 読者別の含意
🔬研究者:Provides a structured overview of ML methods for CCS, useful for identifying research gaps and future directions.
🏢実務担当者:Highlights ML techniques that can reduce operational costs and enhance monitoring in real-world CCS projects.
🏛政策担当者:Offers evidence that ML can improve CCS reliability and reduce costs, supporting policy frameworks for large-scale deployment.
📄 Abstract（原文）
Rising atmospheric CO2 levels pose a critical challenge to achieving global sustainability targets. Geological carbon sequestration (GCS) offers a long-term solution for reducing greenhouse gas emissions, but its large-scale deployment faces limitations in cost, uncertainty, and operational risk. Recent advances in machine learning (ML) present transformative opportunities to enhance every stage of the carbon capture and storage (CCS) lifecycle, from pre-injection evaluation to post-injection monitoring. This review systematically examines ML integration in CCS applications, emphasizing roles in geological characterization, injection optimization, plume prediction, and leakage detection. It provides a structured overview of ML methodologies including Random Forest, Support Vector Regression, and XGBoost, along with emerging deep learning models used for anomaly detection and uncertainty quantification. Experimental insights, monitoring techniques, and real-time data applications are summarized to illustrate ML’s capability in accelerating simulations, reducing costs, and increasing safety assurance. Furthermore, real-world case studies such as Sleipner (Norway), Illinois Basin–Decatur (USA), Boundary Dam (Canada), Gorgon (Australia), and Quest (Canada) demonstrate how ML has enhanced performance, predictive accuracy, and storage reliability in field-scale CCS projects. The review concludes by identifying existing challenges, data scarcity, interpretability, and regulatory integration, and proposes a unified ML framework for scalable, autonomous, and secure CO2 storage. Overall, this study provides a comprehensive roadmap for leveraging artificial intelligence to achieve reliable, cost-effective, and sustainable carbon management solutions aligned with global net-zero objectives.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19133104first seen 2026-07-20 04:48:55
🔔 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。