Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference
持続可能なジオエネルギーのための機械学習：不確実性、物理、意思決定に対応した推論 (AI 翻訳)
Hannah Menke, A. Elsheikh, Lingli Wei, Nanzhe Wang, Andreas Busch
🤖 gxceed AI 要約
日本語
本論文は、CO2貯留・地熱・水素生成貯留・超臨界流体からの重要鉱物採取・核廃棄物処分など、ジオエネルギープロジェクトにおける機械学習（ML）の役割を包括的に論じる。不確実性の定量化、物理情報を融合したML、品質保証・監査可能性といった課題を整理し、意思決定に直結する推論フレームワークを提案する。CCUSや水素など脱炭素技術の実装加速に貢献する。
English
This paper comprehensively discusses the role of machine learning (ML) in geoenergy projects (CO2 storage, geothermal, H2 storage, critical minerals, nuclear waste). It identifies bottlenecks such as scarce labels, uncertainty quantification, scale-bridging, and governance, and proposes hybrid physics-ML, probabilistic UQ, and multi-fidelity learning. The paper connects these to applications like digital twins, multiphase flow, and monitoring, emphasizing decision-ready inference for climate mitigation.
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（苫小牧など）や水素・アンモニア混焼、地熱発電に注力しており、安全性と実効性の担保が課題。本論文の不確実性評価・物理制約下での意思決定アプローチは、日本のジオエネルギープロジェクトの規制枠組みや実証に直接示唆を与える。
In the global GX context
Globally, scaling CO2 storage and hydrogen requires transparent, auditable ML tools for monitoring and verification. This paper outlines a pragmatic agenda for benchmarks, validation, and policy support that aligns with ISSB and CSRD expectations for credible climate claims, offering a technical roadmap for project developers and regulators.
👥 読者別の含意
🔬研究者:Highlights ML-based methods to overcome bottlenecks in geoenergy deployment, providing a roadmap for decision-ready inference under uncertainty.
🏢実務担当者:Offers insights into uncertainty management and QA for geoenergy projects, useful for project developers and operators to improve cost of capital and deployment rate.
🏛政策担当者:Identifies reporting standards and policy support needed for reproducible ML in geoenergy, relevant for regulators overseeing carbon storage and hydrogen projects.
📄 Abstract（原文）
Geoenergy projects (CO2 storage, geothermal, subsurface H2 generation/storage, critical minerals from subsurface fluids, or nuclear waste disposal) increasingly follow a petroleum-style funnel from screening and appraisal to operations, monitoring, and stewardship. Across this funnel, limited and heterogeneous observations must be turned into risk-bounded operational choices under strong physical and geological constraints - choices that control deployment rate, cost of capital, and the credibility of climate-mitigation claims. These choices are inherently multi-objective, balancing performance against containment, pressure footprint, induced seismicity, energy/water intensity, and long-term stewardship. We argue that progress is limited by four recurring bottlenecks: (i) scarce, biased labels and few field performance outcomes; (ii) uncertainty treated as an afterthought rather than the deliverable; (iii) weak scale-bridging from pore to basin (including coupled chemical-flow-geomechanics); and (iv) insufficient quality assurance (QA), auditability, and governance for regulator-facing deployment. We outline machine learning (ML) approaches that match these realities (hybrid physics-ML, probabilistic uncertainty quantification (UQ), structure-aware representations, and multi-fidelity/continual learning) and connect them to four anchor applications: imaging-to-process digital twins, multiphase flow and near-well conformance, monitoring and inverse problems (monitoring, measurement, and verification (MMV), including deformation and microseismicity), and basin-scale portfolio management. We close with a pragmatic agenda for benchmarks, validation, reporting standards, and policy support needed for reproducible and defensible ML in sustainable geoenergy.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2603.14907first seen 2026-07-16 06:18:24
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