Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.14907v1</id>\n    <title>Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference</title>\n    <updated>2026-03-16T07:08:45Z</updated>\n    <link href='https://arxiv.org/abs/2603.14907v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.14907v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.dis-nn'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <published>2026-03-16T07:08:45Z</published>\n    <arxiv:comment>3 figures, 1 table</arxiv:comment>\n    <arxiv:primary_category term='cond-mat.dis-nn'/>\n    <author>\n      <name>Hannah P. Menke</name>\n    </author>\n    <author>\n      <name>Ahmed H. Elsheikh</name>\n    </author>\n    <author>\n      <name>Lingli Wei</name>\n    </author>\n    <author>\n      <name>Nanzhe Wang</name>\n    </author>\n    <author>\n      <name>Andreas Busch</name>\n    </author>\n  </entry>"
}