Simplified training of reservoir simulation models
Project description
FlowNet: Data-Driven Reservoir Predictions
FlowNet aims at solving the following problems:
- Create data-driven reduced physics models - directly from the data
- Train the model
- Assure model predictiveness
- Use the models to efficiently optimize and make decisions
For documentation, see the GitHub pages for this repository.
Contributing
Please check out our contribution guidelines if you want to contribute to FlowNet.
Installation
FlowNet is a Python package. The package itself, and other dependencies, can be installed using a Python virtual environment, except for the OPM-Flow reservoir simulator.
Install FlowNet
FlowNet uses the open-source reservoir simulator OPM-Flow. To be able to run FlowNet you will need to have OPM-Flow
installed first. There are also other dependencies like the Python packages libecl
and
libres
which currently are not easily installable from PyPI (however, things are happening, so hopefully in a not too distant future, dependencies are installable from PyPI, which is already the case for flownet itself: pip install flownet
).
1. Clone the FlowNet GitHub repository with SSH:
git clone git@github.com:equinor/flownet.git
2. Move into the cloned directory:
cd flownet
3. Run the scripts containing the building recipe:
bash ./apt_install.sh
bash ./build_environment.sh ./venv /usr/bin/flow
This will automatically create a simple Python virtual environment ./venv
4. Source the newly created virtual environment:
source ./venv/bin/activate
5. Install the flownet
Python module in development mode:
pip install -e .
Omit the -e
flag if you want a standard installation.
:warning: Do you want to run FlowNet through the LSF queue? To be able to have the ERT process, that will be called by FlowNet, run jobs via LSF correctly you will need to update your default shell's configuration file (
.cshrc
or.bashrc
) to automatically source your virtual environment.
Running FlowNet
You can run FlowNet as a single command line:
flownet ahm ./some_config.yaml ./some_output_folder
Run flownet --help
to see all possible command line argument options.
Running webviz to check results
Before running webviz
for the first time on your machine, you will need to to create a localhost https
certificate by doing:
webviz certificate --auto-install --force
License
FlowNet is, with a few exceptions listed below, GPLv3.
- The Norne test data is available under the Open Database License
- The FlowNet logo is CC BY-NC-ND 4.0
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