ERT - Ensemble based Reservoir Tool - is designed for running ensembles of dynamical models such as reservoir models, in order to do sensitivity analysis and data assimilation. ERT supports data assimilation using the Ensemble Smoother (ES), Ensemble Smoother with Multiple Data Assimilation (ES-MDA) and Iterative Ensemble Smoother (IES).
Python 3.8+ with development headers.
$ pip install ert $ ert --help
or, for the latest development version:
$ pip install git+https://github.com/equinor/ert.git@master $ ert --help
ert program is based on two different repositories:
ecl which contains utilities to read and write Eclipse files.
ert - this repository - the actual application and all of the GUI.
ERT is now Python 3 only. The last Python 2 compatible release is 2.14
Documentation for ert is located at https://ert.readthedocs.io/en/latest/.
ERT uses Python for user-facing code and C++ for some backend code. Python is the easiest to work with and is likely what most developers will work with.
You might first want to make sure that some system level packages are installed before attempting setup:
- pip - python include headers - (python) venv - (python) setuptools - (python) wheel
It is left as an exercise to the reader to figure out how to install these on their respective system.
To start developing the Python code, we suggest installing ERT in editable mode into a virtual environment to isolate the install (substitute the appropriate way of sourcing venv for your shell):
# Create and enable a virtualenv python3 -m venv my_virtualenv source my_virtualenv/bin/activate # Update build dependencies pip install --upgrade pip wheel setuptools # Download and install ERT git clone https://github.com/equinor/ert cd ert pip install --editable .
Trouble with setup
If you encounter problems during install and attempt to fix them, it might be
wise to delete the
_skbuild folder before retrying an install.
Additional development packages must be installed to run the test suite:
pip install -r dev-requirements.txt pytest tests/
As a simple test of your
ert installation, you may try to run one of the
examples, for instance:
cd test-data/local/poly_example # for non-gui trial run ert test_run poly.ert # for gui trial run ert gui poly.ert
Note that in order to parse floating point numbers from text files correctly,
your locale must be set such that
. is the decimal separator, e.g. by setting
# export LC_NUMERIC=en_US.UTF-8
in bash (or an equivalent way of setting that environment variable for your shell).
C++ is the backbone of ERT 2 as in used extensively in important parts of ERT. There's a combination of legacy code and newer refactored code. The end goal is likely that some core performance-critical functionality will be implemented in C++ and the rest of the business logic will be implemented in Python.
--editable will create the necessary Python extension module
res/_lib.cpython-*.so), changing C++ code will not take effect even when
reloading ERT. This requires recompilation, which means reinstalling ERT from
To avoid recompiling already-compiled source files, we provide the
script/build script. From a fresh virtualenv:
git clone https://github.com/equinor/ert cd ert script/build
This command will update
pip if necessary, install the build dependencies,
compile ERT and install in editable mode, and finally install the runtime
requirements. Further invocations will only build the necessary source files. To
do a full rebuild, delete the
Note: This will create a debug build, which is faster to compile and comes with
debugging functionality enabled. This means that, for example, Eigen
computations will be checked and will abort if preconditions aren't met (eg.
when inverting a matrix, it will first check that the matrix is square). The
downside is that this makes the code unoptimised and slow. Debugging flags are
therefore not present in builds of ERT that we release on Komodo or PyPI. To
build a release build for development, use
If pip reinstallation fails during the compilation step, try removing the
The default maximum number of open files is normally relatively low on MacOS and some Linux distributions. This is likely to make tests crash with mysterious error-messages. You can inspect the current limits in your shell by issuing he command
ulimit -a. In order to increase maximum number of open files, run
ulimit -n 16384(or some other large number) and put the command in your
.profileto make it persist.
Testing C code
Install ecl using CMake as a C library. Then:
$ mkdir build $ cd build $ cmake ../libres -DBUILD_TESTS=ON $ cmake --build . $ ctest --output-on-failure
Use the following commands to start developing from a clean virtualenv
$ pip install -r requirements.txt $ python setup.py develop
pip install -e . will also setup ERT for development, but
it will be more difficult to recompile the C library.
scikit-build is used
for compiling the C library. It creates a directory named
_skbuild which is
reused upon future invocations of either
python setup.py develop, or
python setup.py build_ext. The latter only rebuilds the C library. In some cases this
directory must be removed in order for compilation to succeed.
The C library files get installed into
res/.libs, which is where the
res module will look for them.
Basic ert test
To test if ert itself is working, go to
test-data/local/poly_example and start ert by running
cd test-data/local/poly_example ert gui poly.ert
This opens up the ert graphical user interface. Finally, test ert by starting and successfully running the simulation.
ert with a reservoir simulator
To actually get ert to work at your site you need to configure details about
your system; at the very least this means you must configure where your
reservoir simulator is installed. In addition you might want to configure e.g.
queue system in the
site-config file, but that is not strictly necessary for
a basic test.