********
Examples
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Hello world
-----------
In this example we consider the configuration file 'h2o.toml' available in the 
examples folder (`link to the file <https://github.com/cssr-tools/pyopmnearwell/blob/main/examples/h2o.toml>`_), where
the co2store model is used and only water is injected in a radial grid.

If the generated files are to be saved in a folder called 'hello_world', then this is achieved by the following command:

.. code-block:: bash

    pyopmnearwell -i h2o.toml -o hello_world

To visualize the results, this can be achieved by using plopm, for example:

.. code-block:: bash

    plopm -i hello_world/output/H2O -v pressure -s ,,1 -t 'Top view at the end of the simulation' -c bwr -xformat .0f -cformat .0f 

.. figure:: figs/pressure_1D.png

.. tip::
    You can install `plopm <https://github.com/cssr-tools/plopm>`_ by executing in the terminal: pip install git+https://github.com/cssr-tools/plopm.git.


CO2 cyclic injection
--------------------

In this example we consider the configuration file described in the
:doc:`configuration file<./configuration_file>` section, which is available in the 
examples folder as 'co2.toml'.

If the generated files are to be saved in a folder called 'co2', then this is achieved by the following command:

.. code-block:: bash

    pyopmnearwell -i co2.toml -o co2

The execution time was c.a. 20 seconds and the following is an animation using ResInsight to visualize the gas saturation:

.. figure:: figs/saturation.gif

    Simulation results of the gas saturation.

CCUS (machine learning)
-----------------------
See `this folder <https://github.com/cssr-tools/pyopmnearwell/tree/main/examples/cemracs2023/ml_example_co2eor>`_ for an example of
how to use **pyopmnearwell** to generate data for different input parameters (e.g., injection rates) and read the data (e.g., 
production volumes). An additional example can be found in the `data_generation <https://github.com/cssr-tools/pyopmnearwell/tree/main/examples/data_generation>`_ folder. 
These examples could be used as a starting point for the ones interested in ML.

Publications
------------
For the simulation results published in `this paper <https://onepetro.org/SPEBERG/proceedings/24BERG/1-24BERG/D011S012R010/544194>`_ 
about the impact of intermittency on salt precipitation during CO2 injection, see/run 
`these configuration files <https://github.com/cssr-tools/pyopmnearwell/blob/main/publications>`_.

For a study where **pyopmnearwell** is used to generated a machine-learned near-well model, `click here <https://github.com/cssr-tools/ML_near_well>`_.