Platypus: a Python package for automated surrogate-assisted analysis of process-based simulations via the non-intrusive reduced-basis method
- 1GFZ Helmholtz-Zentrum für Geoforschung, Telegrafenberg, 14473 Potsdam, Germany
- 2Institute of Applied Geosciences, TU Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany
Abstract
Modelling the dynamic response of reservoirs to fluid injection often relies on fully coupled thermo-hydro-mechanical models, which provide a high-fidelity representation of the governing physical processes. These models support operational and design decisions under significant geological and parametric uncertainties. However, their high computational cost severely limits their applicability in large-scale statistical analysis and thus limiting the potential to account for these uncertainties. We present Platypus, a Python package that bridges full-order process-based reservoir simulations and surrogate-based analysis. The package provides automated, reproducible workflows covering the full pipeline from parameter sampling and simulation interfacing, through ensemble curation and data management, to surrogate model construction and global sensitivity analysis. Surrogate models are built via the non-intrusive reduced-basis method to deliver computationally efficient, physics-consistent approximations of the transient reservoir response, enabling statistical analysis that require a large number of evaluations. The package is demonstrated on two geothermal applications: a synthetic reservoir benchmark and the Groß Schönebeck geothermal site. These examples illustrate the package's ability to handle time-dependent coupled hydrothermal and thermo-hydro-mechanical processes, manage large simulation ensembles, and perform global sensitivity analyses enabled by surrogates. Platypus is designed to be modular and extensible, providing a flexible tool for ensemble-based reservoir modelling workflows across a range of subsurface applications.
Related
- Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
- Accelerating droplet-laden Stokes flow simulations with hierarchical surrogate modeling
- Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration
- Surrogate-assisted three-dimensional Gross-Pitaevskii characterization of Bose-Einstein condensate transport on an atom chip
- A surrogate-assisted generative framework for on-demand inverse design of phononic metamaterials
BibTeX
@article{hayek2026platypus,
title = {{Platypus: a Python package for automated surrogate-assisted analysis of process-based simulations via the non-intrusive reduced-basis method}},
author = {J. N. Hayek and M. Cacace and M. Faber and Y. Gruzdeva and D. Degen and D. Degen},
journal = {Advances in Geosciences},
year = {2026},
month = nov,
doi = {10.5194/adgeo-68-1-2026},
url = {https://doi.org/10.5194/adgeo-68-1-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - Platypus: a Python package for automated surrogate-assisted analysis of process-based simulations via the non-intrusive reduced-basis method
AU - J. N. Hayek
AU - M. Cacace
AU - M. Faber
AU - Y. Gruzdeva
AU - D. Degen
AU - D. Degen
PY - 2026
DA - 2026/11/04
JO - Advances in Geosciences
DO - 10.5194/adgeo-68-1-2026
UR - https://doi.org/10.5194/adgeo-68-1-2026
AB - Modelling the dynamic response of reservoirs to fluid injection often relies on fully coupled thermo-hydro-mechanical models, which provide a high-fidelity representation of the governing physical processes. These models support operational and design decisions under significant geological and parametric uncertainties. However, their high computational cost severely limits their applicability in large-scale statistical analysis and thus limiting the potential to account for these uncertainties. We present Platypus, a Python package that bridges full-order process-based reservoir simulations and surrogate-based analysis. The package provides automated, reproducible workflows covering the full pipeline from parameter sampling and simulation interfacing, through ensemble curation and data management, to surrogate model construction and global sensitivity analysis. Surrogate models are built via the non-intrusive reduced-basis method to deliver computationally efficient, physics-consistent approximations of the transient reservoir response, enabling statistical analysis that require a large number of evaluations. The package is demonstrated on two geothermal applications: a synthetic reservoir benchmark and the Groß Schönebeck geothermal site. These examples illustrate the package's ability to handle time-dependent coupled hydrothermal and thermo-hydro-mechanical processes, manage large simulation ensembles, and perform global sensitivity analyses enabled by surrogates. Platypus is designed to be modular and extensible, providing a flexible tool for ensemble-based reservoir modelling workflows across a range of subsurface applications.
SN - 1680-7340
SN - 1680-7359
ER -