Platypus: a Python package for automated surrogate-assisted analysis of process-based simulations via the non-intrusive reduced-basis method

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.