ogs5py: A Python‐API for the OpenGeoSys 5 Scientific Modeling Package

Abstract

High‐performance numerical codes are an indispensable tool for hydrogeologists when modeling subsurface flow and transport systems. But as they are written in compiled languages, like C/C++ or Fortran, established software packages are rarely user‐friendly, limiting a wider adoption of such tools. OpenGeoSys (OGS), an open‐source, finite‐element solver for thermo‐hydro‐mechanical–chemical processes in porous and fractured media, is no exception. Graphical user interfaces may increase usability, but do so at a dramatic reduction of flexibility and are difficult or impossible to integrate into a larger workflow. Python offers an optimal trade‐off between these goals by providing a highly flexible, yet comparatively user‐friendly environment for software applications. Hence, we introduce ogs5py, a Python‐API for the OpenGeoSys 5 scientific modeling package. It provides a fully Python‐based representation of an OGS project, a large array of convenience functions for users to interact with OGS and connects OGS to the scientific and computational environment of Python.

From Dynamic Groundwater Level Measurements to Regional Aquifer Parameters— Assessing the Power of Spectral Analysis

“…Setting up model domains including mesh generation, application of boundary conditions and groundwater recharge, and sampling aquifer parameters from large ensembles is tedious work. With ogs5py (Müller, 2019) API it is possible to generate OGS5 input files and run the current setup with python scripts. Furthermore, the output in form of VTK and TecPlot can be read and is directly available in python for further processing.…”

A field evidence model: how to predict transport in heterogeneous aquifers at low investigation level

“…Flow and transport are calculated making use of the finite element solver OpenGeoSys (Kolditz et al, 2012) in the ogs5py Python framework (Müller et al, 2020). The simulation domain is a 2D cross section within x ∈ [−20, 200] m and z ∈ [52, 62] m, generously comprising the area of the MADE-1 tracer experiment (Boggs et al, 1992).…”

“…Aside from qualitative approaches for multi-scale heterogeneity representation, e.g. Neton et al (1994), Herweijer (1997), or Koltermann and Gorelick (1996) (and references therein), only few quantitative approaches were proposed, such as generating sequences of facies assemblages using indicator geostatistics and transition probability at various scales (Weissmann and Fogg, 1999;Proce et al, 2004) or combining training images for large-scale facies realizations with variogram-based geostatistical methods for random intrafacies permeability (Huysmans and Dassargues, 2009). Both approaches show a high level of model complexity and required (hydro)geological input data.…”

GSTools v1.3: A toolbox for geostatistical modelling in Python

“…Salient features of GSTools are its random field generation and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige (Murphy et al, 2021), ogs5py (Müller et al, 2020) or scikit-gstat (Mälicke, 2021), and provides interfaces to meshio (Schlömer et al, 2021) and PyVista (Sullivan and Kaszynski, 2019). The GeoStat-Examples (https://github.com/GeoStat-Examples) provide a number of applications, including the four presented workflows.…”

“…For improved handling of spatial random fields as input for PDE-solvers like the Finite Element Method (FEM), GSTools provides an interface for a number of mesh standards, such as meshio (Schlömer et al, 2021), PyVista (Sullivan and Kaszynski, 2019) and ogs5py (Müller et al, 2020). When using meshio or PyVista, the generated fields will be stored immediately in the mesh container.…”