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PET: Python Ensemble Toolbox


PET is a toolbox for ensemble-based Data Assimilation and Optimisation. It is developed and maintained by the eponymous group at NORCE Norwegian Research Centre AS.

CI status

Installation

Before installing ensure you have python3 pre-requisites. On a Debian system run:

sudo upt-get update
sudo apt-get install python3
sudo apt-get install python3-pip
sudo apt-get install python3-venv

To install PET, first clone the repo (assuming you have added the SSH key)

git clone git@github.com:Python-Ensemble-Toolbox/PET.git PET

Make sure you have the latest version of pip and setuptools:

python3 -m pip install --upgrade pip setuptools

Optionally (but recommended): Create and activate a virtual environment:

python3 -m venv venv-PET
source venv-PET/bin/activate

Some additional features might be not part of your default installation and need to be set in the Python (virtual) environment manually:

python3 -m pip install wheel

If you do not install PET inside a virtual environment, you may have to include the --user option in the following (to install to your local Python site packages, usually located in ~/.local).

Inside the PET folder, run

python3 -m pip install -e .
  • The dot is needed to point to the current directory.
  • The -e option installs PET such that changes to it take effect immediately (without re-installation).

Examples

PET needs to be set up with a configuration file. See the example repository for inspiration.

Simulation wrappers

To use the subsurface simulators Eclipse or OPM, you need to install the SimulatorWrap repository. This repository also contains instructions on how to link your own simulator to PET.

Visualization

Some basic plotting functionality is provided here. The functions should be copied and adapted for each specific use cases.

Tutorials

  • A PIPT tutorial is found here
  • A POPT tutorial is found here

Suggested readings:

If you use PET in a scientific publication, we would appreciate it if you cited one of the first papers where the PET was introduced. Each of them describes some of the PET's functionalities:

Bayesian data assimilation with EnRML and ES-MDA for History-Matching Workflow with AI-Geomodeling

Cite as

Fossum, Kristian, Sergey Alyaev, and Ahmed H. Elsheikh. "Ensemble history-matching workflow using interpretable SPADE-GAN geomodel." First Break 42.2 (2024): 57-63. https://doi.org/10.3997/1365-2397.fb2024014

@article{fossum2024ensemble,
  title={Ensemble history-matching workflow using interpretable SPADE-GAN geomodel},
  author={Fossum, Kristian and Alyaev, Sergey and Elsheikh, Ahmed H},
  journal={First Break},
  volume={42},
  number={2},
  pages={57--63},
  year={2024},
  publisher={European Association of Geoscientists \& Engineers},
  url = {https://doi.org/10.3997/1365-2397.fb2024014}
}

Bayesian inversion technique, localization, and data compression for history matching of the Edvard Grieg field using 4D seismic data

Cite as

Lorentzen, R.J., Bhakta, T., Fossum, K. et al. Ensemble-based history matching of the Edvard Grieg field using 4D seismic data. Comput Geosci 28, 129–156 (2024). https://doi.org/10.1007/s10596-024-10275-0

@article{lorentzen2024ensemble,
  title={Ensemble-based history matching of the Edvard Grieg field using 4D seismic data},
  author={Lorentzen, Rolf J and Bhakta, Tuhin and Fossum, Kristian and Haugen, Jon Andr{\'e} and Lie, Espen Oen and Ndingwan, Abel Onana and Straith, Knut Richard},
  journal={Computational Geosciences},
  volume={28},
  number={1},
  pages={129--156},
  year={2024},
  publisher={Springer},
  url={https://doi.org/10.1007/s10596-024-10275-0}
}

Offshore wind farm layout optimization using ensemble methods

Cite as

Eikrem, K.S., Lorentzen, R.J., Faria, R. et al. Offshore wind farm layout optimization using ensemble methods. Renewable Energy 216, 119061 (2023). https://www.sciencedirect.com/science/article/pii/S0960148123009758

@article{Eikrem2023offshore,
title = {Offshore wind farm layout optimization using ensemble methods},
journal = {Renewable Energy},
volume = {216},
pages = {119061},
year = {2023},
issn = {0960-1481},
doi = {https://doi.org/10.1016/j.renene.2023.119061},
url = {https://www.sciencedirect.com/science/article/pii/S0960148123009758},
author = {Kjersti Solberg Eikrem and Rolf Johan Lorentzen and Ricardo Faria and Andreas St{\o}rksen Stordal and Alexandre Godard},
keywords = {Wind farm layout optimization, Ensemble optimization (EnOpt and EPF-EnOpt), Constrained optimization, Levelized cost of energy (LCOE), Floating offshore wind},
}