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.
Please check out our contribution guidelines if you want to contribute to FlowNet.
FlowNet is a Python package. All required dependencies are automatically installed together with FlowNet, except for the OPM-Flow reservoir simulator binaries which you will need to install separately.
If your Flow installation is not located at /usr/bin/flow you should set an
environment variable FLOW_PATH with path to your Flow executable prior to running
FlowNet.
The easiest and recommended approach is to install FlowNet from PyPI by running
pip install flownet
If you want to install and try out the latest unreleased code you can do
git clone [email protected]:equinor/flownet.git
cd flownet
pip install -e .Omit the -e flag if you want a standard installation.
⚠️ 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 (.cshrcor.bashrc) to automatically source your virtual environment.
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.
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 --forceFlowNet 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
