Simplified and flexible testing framework for near-well simulations via a configuration file using the OPM Flow simulator.

You will first need to install

• OPM Flow (https://opm-project.org, Release 2024.10 or current master branches)

To install the pyopmnearwell executable from the development version:

pip install git+https://github.com/cssr-tools/pyopmnearwell.git

If you are interested in a specific version (e.g., v2024.10) or in modifying the source code, then you can clone the repository and install the Python requirements in a virtual environment with the following commands:

# Clone the repo
git clone https://github.com/cssr-tools/pyopmnearwell.git
# Get inside the folder
cd pyopmnearwell
# For a specific version (e.g., v2024.10), or skip this step (i.e., edge version)
git checkout v2024.10
# Create virtual environment
python3 -m venv vpyopmnearwell
# Activate virtual environment
source vpyopmnearwell/bin/activate
# Upgrade pip, setuptools, and wheel
pip install --upgrade pip setuptools wheel
# Install the pyopmnearwell package
pip install -e .
# For contributions/testing/linting, install the dev-requirements
pip install -r dev-requirements.txt

See the installation for further details on building OPM Flow from the master branches in Linux, Windows, and macOS, as well as the opm Python package and LaTeX dependencies.

You can run pyopmnearwell as a single command line:

pyopmnearwell -i configuration_file.txt

Run pyopmnearwell --help to see all possible command line argument options. Inside the configuration_file.txt file you provide the path to the flow executable and simulation parameters. See the .txt files in the examples, tests/geometries/, and tests/models/ folders.

See the examples in the documentation.

If you would like to cite this repository:

• Landa-Marbán, D. and von Schultzendorff, P.M. 2023. pyopmnearwell: A framework to simulate near well dynamics using OPM Flow. https://doi.org/10.5281/zenodo.10266790.

The following is a list of manuscripts in which pyopmnearwell is used:

1. Landa-Marbán, D., Zamani, N., Sandve, T.H., Gasda, S.E., 2024. Impact of Intermittency on Salt Precipitation During CO2 Injection, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. doi: 10.2118/218477-MS.
2. von Schultzendorff, P., Sandve, T.H., Kane, B., Landa-Marbán, D., Both, J.W., Nordbotten, J.M., 2024. A Machine-Learned Near-Well Model in OPM Flow, presented at ECMOR 2024, European Association of Geoscientists & Engineers, Sep. 2024, pp. 1–23. doi: 10.3997/2214-4609.202437033.
3. Lliguizaca, J.R., Landa-Marbán, D., Gasda, S.E., Sandve, T.H., Alcorn, Z.P., 2024. Data-Driven Predictions of CO2 EOR Numerical Studies Using Machine Learning in an Open-Source Framework, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. doi: 10.2118/218441-MS.
4. Mushabe, R., Minougou, J.D., Landa-Marbán, D., Kane, B., Sandve, T.H., Submitted. Predicting Ultimate Hydrogen Production and Residual Volume during Cyclic Underground Hydrogen Storage in Porous Media using Machine Learning.

The pyopmnearwell package is being funded by the HPC Simulation Software for the Gigatonne Storage Challenge project [project number 622059] and Center for Sustainable Subsurface Resources (CSSR) [project no. 331841]. This is work in progress. Contributions are more than welcome using the fork and pull request approach. For a new feature, please request this by raising an issue.