Emerging applications of physics-informed and physics-guided machine learning in geoenergy science: A review; Communications in Nonlinear Science and Numerical Simulation; Vol. 154
| Parent link: | Communications in Nonlinear Science and Numerical Simulation.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 154.— 2026.— Article number 109551, 27 p. |
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| Outros autores: | , , , , , |
| Summary: | Title screen The complexity and sustainability demands of geoenergy science (GS) systems necessitate predictive models that are both accurate and computationally efficient. Traditional numerical simulators, while physically rigorous, are often prohibitively expensive, whereas purely data-driven machine learning models lack interpretability and physical fidelity, especially under data scarcity. Physics-informed machine learning (PIML) bridges this gap by integrating deep learning with governing physical laws to deliver reliable, efficient, and interpretable predictions. This review provides a comprehensive analysis of PIML applications in GS, with emphasis on reservoir modeling, subsurface multiphase flow, thermal dynamics, and inter-well connectivity estimation. It critically evaluates core PIML paradigms, including physics-informed neural networks (PINNs), neural operators (NO), and physics-guided machine learning (PGML), across key domains: underground gas storage (natural gas, CO₂, and H₂), geothermal energy systems, and real-time reservoir optimization. The review highlights how PIML embeds conservation laws (e.g., mass, energy) to solve both forward and inverse problems, and assesses advances in uncertainty quantification, transfer learning, and multiscale hierarchical modeling. Real-world case studies and bibliometric trends illustrate practical deployment and emerging methodological directions. PIML offers the potential to provide robust, generalizable frameworks that enhance prediction performance, ensure physical consistency, and substantially reduce computational cost, even with sparse or noisy field data. The principal outcome of this review is that PIML enables generalizable, physics-compliant surrogate models that outperform conventional approaches. However, future work is required to prioritize scalable architectures, resilience to data noise, and tighter integration with downhole sensing to support autonomous, physics-guided decision-making in the next-generation of GS operations Текстовый файл AM_Agreement |
| Idioma: | inglés |
| Publicado: |
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.1016/j.cnsns.2025.109551 |
| Formato: | Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=687153 |
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| 200 | 1 | |a Emerging applications of physics-informed and physics-guided machine learning in geoenergy science: A review |f Shadfar Davoodi, David A. Wood, Mohammed Al-Shargabi [et al.] | |
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| 330 | |a The complexity and sustainability demands of geoenergy science (GS) systems necessitate predictive models that are both accurate and computationally efficient. Traditional numerical simulators, while physically rigorous, are often prohibitively expensive, whereas purely data-driven machine learning models lack interpretability and physical fidelity, especially under data scarcity. Physics-informed machine learning (PIML) bridges this gap by integrating deep learning with governing physical laws to deliver reliable, efficient, and interpretable predictions. This review provides a comprehensive analysis of PIML applications in GS, with emphasis on reservoir modeling, subsurface multiphase flow, thermal dynamics, and inter-well connectivity estimation. It critically evaluates core PIML paradigms, including physics-informed neural networks (PINNs), neural operators (NO), and physics-guided machine learning (PGML), across key domains: underground gas storage (natural gas, CO₂, and H₂), geothermal energy systems, and real-time reservoir optimization. The review highlights how PIML embeds conservation laws (e.g., mass, energy) to solve both forward and inverse problems, and assesses advances in uncertainty quantification, transfer learning, and multiscale hierarchical modeling. Real-world case studies and bibliometric trends illustrate practical deployment and emerging methodological directions. PIML offers the potential to provide robust, generalizable frameworks that enhance prediction performance, ensure physical consistency, and substantially reduce computational cost, even with sparse or noisy field data. The principal outcome of this review is that PIML enables generalizable, physics-compliant surrogate models that outperform conventional approaches. However, future work is required to prioritize scalable architectures, resilience to data noise, and tighter integration with downhole sensing to support autonomous, physics-guided decision-making in the next-generation of GS operations | ||
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| 463 | 1 | |t Vol. 154 |v Article number 109551, 27 p. |d 2026 | |
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| 610 | 1 | |a Physics-informed machine learning (PIML) | |
| 610 | 1 | |a Geoenergy science systems | |
| 610 | 1 | |a Underground gas storage | |
| 610 | 1 | |a Reservoir modeling | |
| 610 | 1 | |a Geothermal energy | |
| 610 | 1 | |a Uncertainty quantification | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |9 22200 | |
| 701 | 1 | |a Wood |b D. A. |g David | |
| 701 | 1 | |a Al-Shargabi |b M. A. T. S. |c specialist in the field of petroleum engineering |c Engineer of Tomsk Polytechnic University |f 1993- |g Mokhammed Abdulsalam Takha Sallam |9 22768 | |
| 701 | 1 | |a Vanovskiy |b V. |g Vladimir | |
| 701 | 1 | |a Rukavishnikov |b V. S. |c Director of the Center for Training and Retraining of Oil and Gas Specialists, Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences |c Engineer of Tomsk Polytechnic University |f 1984- |g Valery Sergeevich |9 17614 | |
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