iterative-ensemble-smoother 0.6.0

A library for the iterative ensemble smoother algorithm.

Iterative Ensemble Smoother

About

iterative_ensemble_smoother is a Python library for data assimilation and history matching using ensemble-based methods. It implements efficient algorithms particularly effective for problems with a large number of parameters (e.g., millions) and relatively few realizations (e.g., hundreds).

The package provides two main algorithms:

• ESMDA (Ensemble Smoother with Multiple Data Assimilation) - A non-iterative method with multiple data assimilation steps, described in Emerick & Reynolds 2013
• SIES (Subspace Iterative Ensemble Smoother) - An iterative Gauss-Newton method described in Evensen et al. 2019

The package also supports two methods of localization: correlation-based (AdaptiveESMDA) and distance-based (DistanceESMDA).

Installation

iterative_ensemble_smoother is on PyPi and can be installed using pip:

pip install iterative_ensemble_smoother

If you want to do development, then run:

git clone https://github.com/equinor/iterative_ensemble_smoother.git
cd iterative_ensemble_smoother
<create environment>
pip install --editable '.[doc,dev]'

Usage

iterative_ensemble_smoother mainly implements the two classes SIES and ESMDA. Check out the examples section to see how to use them.

Building the documentation

apt install pandoc # Pandoc is required to build the documentation.
pip install .[doc]
sphinx-build -c docs/source/ -b html docs/source/ docs/build/html/

Releasing a new version

• Create a tag, e.g. git tag -a v1.0.0 -m "A short note" cf2c87270d3 locally on the commit.
• Push the tag, e.g. git push upstream v1.0.0.
• Create a release on the GitHub GUI.