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. 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.
Install 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 git@github.com:equinor/flownet.git
cd flownet
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 (
.cshrcor.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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