Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference

2026-03-16

AI Summary

This work addresses key challenges in the sustainable development of geothermal energy, including data scarcity, inadequate uncertainty quantification, weak multi-scale physical coupling, and lack of auditable decision-making. To overcome these limitations, we propose a hybrid framework that integrates physical principles with machine learning, placing uncertainty quantification at its core. By incorporating physics-informed constraints and structure-aware representations, the approach enables decision-oriented uncertainty characterization. Leveraging physics-informed machine learning, probabilistic modeling, and multi-fidelity continual learning, our method supports cross-scale modeling—from pore to basin—and facilitates multi-objective optimization. The framework is demonstrated across four critical applications: digital twins, multiphase flow simulation, monitoring and inversion, and portfolio resource management, delivering reproducible, auditable, and policy-relevant decision support with quantified risk control.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Stochastic OptimizationKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoTGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• geoenergy
• uncertainty
• multi-objective decision-making
• physics constraints
• risk-bounded inference

Innovation

• physics-informed machine learning
• uncertainty quantification
• multi-fidelity learning
• digital twins
• geoscientific decision-making