SCREEN

This repository contains source codes and documentation for SCREEN project.

Clone the repository

Locate a folder at your local machine that you intend to investigate the codes, and then clone the repository git clone https://github.com/equinor/SCREEN By this time you should have a folder named SCREEN at your local machine. Now change the directory with linux command: cd SCREEN

It's normal for us to make a new branch if we indend to make some changes of the codes. This can be done with the -b option, for example: git checkout -b xyz/cleanup This would generate a new branch, named xyz/cleanup. Here the branch name is created by concatenating a short name, such as xyz, of equinor account with a feature description cleanup. There is no need to follow this convention. You could simply pick any branch name as long as it makes sense. However, please note branch names have limitations.

Virtual environment

It's a common practice to work on a project within a python virtual environment. I have been using python's builtin module venv for a long while. So I am going to stick to it here as an example to set up the virtual environment. But you are free to use any other virtual environment setups that you feel comfortable with, such as conda, etc. python -m venv venv_screen This will build a virtual environment venv_screen. You only need to creat it once.

To activate this venv_screen, run the following command: source venv_screen/bin/activate.csh when your linux Shell is csh.

If you are using bash or plain sh, you can activate it with the following command: source venv_screen/bin/activate

We pack needed python packages into a file, such as requirements.txt. And install those python packages to this virtual environment by running the following command: pip install -r requirements.txt You should now be ready to play with the source codes.

Poetry for dependency management

poetry, as an alternative tool for dependency management, is required in Equinor to be compliant with IT policy.

1. Installation of poetry

In case that poetry is not available in the system, you may have to download and install it yourself. Somehow the newer version of poetry doesn't work with python 3.8. So we have to get an earlier of poetry, such as 1.2.0 with the following command: curl -sSL https://install.python-poetry.org | python3 - --version 1.2.0 This should install poetry in your home directory. For example, in my case, it is installed at /private/hzh/.local/bin. Make sure to set this path as an environment variable in your .cshrc or .bashrc file so that you can access poetry from anywhere.

You can test that everything is set up by executing:

poetry --version

2. Installation of python dependencies

To install python dependencies, run the following command*: poetry install

To check what packages have been installed, try the following command: poetry show --tree

To show where the virtual environment is located, run poetry env info This will display the python path for the activated virtual environment. The executable path can be used in VS Code to set up python interpreter path for builtin jupyter notebooks.

The virtual envrionment can be activated with the command*: poetry shell This generated shell will be used to run standalone python scripts.

Note: For whatever reason, in case you decide to uninstall poetry package, you can do it with curl -sSL https://install.python-poetry.org | python3 - --uninstall

Experiments

There are at least two ways to make experimenal runs of the codes. One is to run the experiments with Jupyter lab, and the other is commandline option. While Jupyter notebooks are mainly for QC tests and research purposes, the commandline option is aiming for production run.

1. Jupyter notebooks

Jupyter notebooks are located in directory notebooks. To test its functionaries, change current directory to notebooks and launch jupyter notebooks at the commandline: jupyter-lab Or if you prefer, you can run these Jupyter notebooks from Microsoft's VS code.

There exist several Jupyter notebooks in the directory:

• Notebook GaP-WellClass.ipynb is a test example for the integration of GaP and WellClass. It hides many details. It require eclipse .EGRID and .INIT as input files. This notebook also serves the role of generating pytest data for unit testing.
• Notebook pflotran-gap.ipynb integrates GaP and WellClass too. But instead of the user-provided .EGRID and .INIT files, both files are generates by calling pflotran scripts.
• Notebook Pressure-WellClass_test.ipynb is Alejandro's tests on deviated wells.
• Notebook Pressure-WellClass.ipynb is used to test pressure.
• Notebook WellClasscsvyaml.ipynb is used to test pressure and loading .csv and .yaml input files.

2. Commandline option

Two python scripts for commandline option are available in directory experiments. One script, gap_plotran.py, can be used not only for generating Eclipse .EGRID and .INIT on the fly but also can be used for quick pflotran test, while the other script, gap_wellclass.py, requires the user to provide these two grid files.

The followings are some of the sample runs. In either way, you should run the python script inside the SCREEN directory.

1. To test gap_wellclass.py, run either of the followings: ``` # 1. for smeaheia_v1

python -m experiments.gapwellclass --sim-path ./testdata/examples/smeaheiav1 --well smeaheia.yaml --sim-case GENNOLGR_PH2 --plot

2. for smeaheia_v2

python -m experiments.gapwellclass --sim-path ./testdata/examples/smeaheia_v2 --well smeaheia.yaml --sim-case TEMP-0 --plot

3. for wildcat

python -m experiments.gapwellclass --sim-path ./testdata/examples/wildcat --well wildcat.yaml --sim-case TEMP-0 --plot

`` This will generate an output fileLEG_HIRES.grdeclinexperiments` directory.

1. To test gap_plotran.py, run the following commnad at the directory SCREEN: python python -m experiments.gap_pflotran \ --sim-path ./test_data/examples/wildcat-pflotran \ --well wildcat.yaml \ --sim-case1 TEMP-0_NOSIM \ --sim-case2 TEMP-0 \ --plot ### 3. Test data In order for a quick test of the codes, we include some test dataset in the folder test_data/examples. The input data structure is organized similiar to the pflotran. For example, for test data test_data/examples/wildcat-pflotran-2, the input file structure should be like this: ├── wildcat.yaml ├── include │   ├── co2_db_new.dat │   ├── temperature_gradient.inc │   ├── TEMP_GRD.grdecl │   ├── TEMP_GRD_NOSIM.grdecl │   └── tops_dz.inc └── model ├── TEMP-0.in └── TEMP-0_NOSIM.in Sub-directories, such as wildcat, smeaheia_v1 and smeaheia_v2, contain the necessary data, e.g., Eclipse .EGRID and .INIT files, for testing gap_wellclass.py.

One sub-directory, wildcat-plotran, contains configuration parameters for testing gap_pflotran.py, i.e., use pfloatran to generate .EGRID and .INIT.

Another sub-directory frigg contains information for testing deviated wells.

In addition, the PVT values are included in the directory pvt_contants for self-consistent testing of pressure-related computes.

Unit testing and code coverage

We are using pytest for unit testing and code coverage. The unit testing utilizes wildcat as the testing example. So please make sure the saved .pkl files in test_data/examples/wildcat/pytest exists and is updated. Here is a commandline example: pyton python -m pytest tests This will report the unit testing results. And the following will report not only unit testing but also code coverage: python python -m pytest --cov tests or a litle bit more complex command: python python -m pytest --cov --cov-branch --cov-report term-missing tests

Documentation

The document can be automatically generated and deployed to github pages. To do that, type the following at the command line: mkdocs gh-deploy It may take some minutes until the documentation goes live. And the generated documentation page can be found at SCREEN docs.

The code structures

The following represents the current code structures:

├── experiments │   ├── gap_pflotran.py │   ├── gap_wellclass.py │   ├── __init__.py │   └── LEG_HIRES.grdecl ├── INSTALLATION.md ├── mkdocs.yml ├── notebooks │   ├── GaP-WellClass.ipynb │   ├── LEG_HIRES.grdecl.smeaheia │   ├── pflotran_gap.ipynb │   ├── Pressure-WellClass.ipynb │   ├── Pressure-WellClass_test.ipynb │   └── WellClass_csv_yaml.ipynb ├── README.md ├── requirements.txt ├── src │   ├── GaP │   │   ├── data │   │   ├── experiments │   │   ├── __init__.py │   │   ├── libs │   │   ├── notebooks │   │   └── README.md │   ├── __init__.py │   ├── PressCalc │   │   ├── 1D_PresCalc.ipynb │   │   ├── __init__.py │   │   ├── phase_envelope.png │   │   ├── Pressure_plot.png │   │   ├── PT_01012996 │   │   ├── PT_010153 │   │   └── Readme.md │   ├── WellClass │   │   ├── __init__.py │   │   ├── libs │   │   ├── notebooks │   │   └── README.md │   └── WellViz │   ├── __init__.py │   ├── Readme.md │   └── WellViz_Jan23_Dash_v4.py └── test_data ├── examples │   ├── wildcat │   ├── wildcat-pflotran │ ├── wildcat-pflotran-2 │   ├── frigg │   ├── simple_well │   ├── smeaheia_v1 │   └── smeaheia_v2 └── pvt_constants ├── pressure.txt ├── rho_co2.txt ├── rho_h2o.txt └── temperature.txt It was generated with the linux command tree: shell tree -I 'docs|site|venv_screen|*pycache*|Equinor*|originals' -L 3