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.

翻译：地热能源项目（包括二氧化碳封存、地热能开发、地下氢气生成与储存、从地下流体中提取关键矿物以及核废料处置）正日益遵循石油工业式的漏斗型流程，从筛选评估到运营、监测及长期管理。在此流程中，必须在严格的物理与地质约束下，将有限且异质的观测数据转化为风险可控的运营决策——这些决策直接影响部署速率、资本成本以及气候减缓承诺的可信度。此类决策本质上是多目标优化的过程，需在工程性能与封存安全性、压力影响范围、诱发地震活动、能源/水资源消耗强度以及长期管理之间取得平衡。我们认为当前进展主要受限于四个反复出现的瓶颈：(i) 稀缺且存在偏差的标注数据及少量现场性能结果；(ii) 不确定性常被事后处理而非作为核心交付成果；(iii) 从孔隙尺度到盆地尺度的跨尺度耦合薄弱（涵盖化学-流动-地质力学耦合过程）；(iv) 面向监管部署的质量保证、可审计性与治理机制不足。本文系统阐述了匹配这些现实需求的机器学习方法（物理-机器学习混合建模、概率不确定性量化、结构感知表征、多保真度/持续学习），并将其与四个核心应用领域相联结：成像-流程数字孪生、多相流与近井筒一致性控制、监测与反演问题（包含形变与微震监测的MMV体系），以及盆地尺度资产组合管理。最后，我们提出一套务实的行动议程，涵盖基准测试、验证体系、报告标准与政策支持，以推动可持续地热能源领域可复现、可验证的机器学习方法发展。