Updates to 2024.04

.gitignore

@@ -191,6 +191,7 @@ prototyping/
 cssr/
 presentation/
 paper/
+playground/
 project/
 .vscode/
 

MANIFEST.in

@@ -7,7 +7,5 @@ include src/pyopmnearwell/templates/co2store/*
 include src/pyopmnearwell/templates/common/*
 include src/pyopmnearwell/templates/h2store/*
 include src/pyopmnearwell/templates/saltprec/*
-include src/pyopmnearwell/templates/standardwell/*
-include src/pyopmnearwell/templates/standardwell_impl/*
 include src/pyopmnearwell/utils/*
 include src/pyopmnearwell/visualization/*

README.md

@@ -1,5 +1,5 @@
 [![Build Status](https://github.com/cssr-tools/pyopmnearwell/actions/workflows/CI.yml/badge.svg)](https://github.com/cssr-tools/pyopmnearwell/actions/workflows/CI.yml)
-<a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10%20|%203.11-blue.svg"></a>
+<a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10%20|%203.11|%203.12-blue.svg"></a>
 [![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 [![DOI](https://zenodo.org/badge/662625461.svg)](https://zenodo.org/doi/10.5281/zenodo.10266790)
@@ -13,9 +13,16 @@ using the [_OPM-Flow_](https://opm-project.org/?page_id=19) simulator.
 
 ## Installation
 You will first need to install
-* Flow (https://opm-project.org, Release 2023.10 or current master branches)
+* OPM Flow (https://opm-project.org, Release 2024.04 or current master branches)
 
-You can install the requirements in a virtual environment with the following commands:
+To install the _pyopmnearwell_ executable in an existing Python environment: 
+
+```bash
+pip install git+https://github.com/cssr-tools/pyopmnearwell.git
+```
+
+If you are interested in modifying the source code, then you can clone the repository and 
+install the Python requirements in a virtual environment with the following commands:
 
 ```bash
 # Clone the repo
@@ -28,16 +35,14 @@ python3 -m venv vpyopmnearwell
 source vpyopmnearwell/bin/activate
 # Upgrade pip, setuptools, and wheel
 pip install --upgrade pip setuptools wheel
-# Install the pyopmnearwell package (in editable mode for contributions/modifications; otherwise, pip install .)
+# Install the pyopmnearwell package
 pip install -e .
 # For contributions/testing/linting, install the dev-requirements
 pip install -r dev-requirements.txt
 ``` 
 
-See the [_CI.yml_](https://github.com/cssr-tools/pyopmnearwell/blob/main/.github/workflows/CI.yml) script 
-for installation of OPM Flow (binary packages) and the pyopmnearwell package. If you are a Linux user (including the windows subsystem for Linux), then you could try to build Flow from the master branches with mpi support, by running the script `./build_opm-flow_mpi.bash`, which in turn should build flow in the folder ./build/opm-simulators/bin/flow. 
-
-For macOS users with the latest chips (M1/M2, guessing also M3?), the ecl, resdata, and opm Python packages are not available via pip install. Then before installation, remove them from the `requirements.txt`, then proceed with the Python requirements installation, install the OPM Flow dependencies (using macports or brew), and once inside the vpyopmnearwell Python environment, run the `./build_opm-flow_macOS.bash`, and deactivate and activate the virtual environment (this script builds OPM Flow as well as the opm Python package, and it exports the required PYTHONPATH).
+See the [_installation_](https://cssr-tools.github.io/pyopmnearwell/examples.html) for further details on building OPM Flow from the master branches
+in Linux, Windows, and macOS.
 
 ## Running pyopmnearwell
 You can run _pyopmnearwell_ as a single command line:
@@ -47,11 +52,10 @@ pyopmnearwell -i some_input.txt -o some_output_folder
 Run `pyopmnearwell --help` to see all possible command line 
 argument options. Inside the `some_input.txt` file you provide the path to the
 flow executable and simulation parameters. See the .txt files in the `examples/`,
-`tests/geometries/`, and `tests/models/` folders. For macOS users, then use the flag
-`-p opm` for plotting (resdata is the default one).
+`tests/geometries/`, and `tests/models/` folders. 
 
 ## Getting started
-See the [_documentation_](https://cssr-tools.github.io/pyopmnearwell/introduction.html).
+See the [_examples_](https://cssr-tools.github.io/pyopmnearwell/examples.html) in the [_documentation_](https://cssr-tools.github.io/pyopmnearwell/introduction.html).
 
 ## About pyopmnearwell
 The pyopmnearwell package is being funded by the [_HPC Simulation Software for the Gigatonne Storage Challenge project_](https://www.norceresearch.no/en/projects/hpc-simulation-software-for-the-gigatonne-storage-challenge) [project number 622059] and [_Center for Sustainable Subsurface Resources (CSSR)_](https://cssr.no) 

dev-requirements.txt

@@ -1,7 +1,7 @@
 black
-pylint
+numpydoc
 mypy
+pylint
 pytest-cov
 sphinx
-sphinx-rtd-theme
-pytest
+sphinx-rtd-theme

docs/_sources/api.rst.txt

@@ -7,8 +7,8 @@ The scripts in the utils folder process the input configuration file, creates th
 write the input files by using the scripts in the templates folder, and execute OPM Flow. The scripts in 
 the visualization folder contains files for the postprocessing of the results to generate figures (.png) to show
 comparisons between the different runs/parts of the geological model such as well injectivity (WI) and
-2D spatial maps for the last time step simulated. The ml folder is under development, see the files in the 
-'examples/cemracs2023/peaceman_well-model_nn' if you are curious.
+2D spatial maps for the last time step simulated. The ml folder constains scripts used in 
+`this repository <https://github.com/cssr-tools/ML_near_well>`_.
 
 .. figure:: figs/contents.png
 

docs/_sources/examples.rst.txt

@@ -75,3 +75,11 @@ The following are some of the plots created by the **pyopmnearwell** executable:
 
     Permeability (top), CO2 injection schedule (middle), and saturation values over time on the cells along the well location
     at three different locations (bottom). 
+
+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>`_. 

docs/_sources/index.rst.txt

@@ -7,6 +7,7 @@ Welcome to pyopmnearwell's documentation!
    :maxdepth: 4
 
    introduction
+   installation
    configuration_file
    examples
    api

docs/_sources/installation.rst.txt

@@ -0,0 +1,128 @@
+============
+Installation
+============
+
+Python package
+--------------
+
+To install the **pyopmnearwell** executable in an existing Python environment: 
+
+.. code-block:: bash
+
+    pip install git+https://github.com/cssr-tools/pyopmnearwell.git
+
+If you are interested in modifying the source code, then you can clone the repository and 
+install the Python requirements in a virtual environment with the following commands:
+
+.. code-block:: console
+
+    # Clone the repo
+    git clone https://github.com/cssr-tools/pyopmnearwell.git
+    # Get inside the folder
+    cd pyopmnearwell
+    # Create virtual environment
+    python3 -m venv vpyopmnearwell
+    # Activate virtual environment
+    source vpyopmnearwell/bin/activate
+    # Upgrade pip, setuptools, and wheel
+    pip install --upgrade pip setuptools wheel
+    # Install the pyopmnearwell package
+    pip install -e .
+    # For contributions/testing/linting, install the dev-requirements
+    pip install -r dev-requirements.txt
+
+OPM Flow
+--------
+You also need to install:
+
+* OPM Flow (https://opm-project.org, Release 2024.04 or current master branches)
+
+.. tip::
+
+    See the `CI.yml <https://github.com/cssr-tools/pyopmnearwell/blob/main/.github/workflows/CI.yml>`_ script 
+    for installation of OPM Flow (binary packages) and the pyopmnearwell package in Linux. 
+
+Source build in Linux/Windows
++++++++++++++++++++++++++++++
+If you are a Linux user (including the Windows subsystem for Linux), then you could try to build Flow (after installing the `prerequisites <https://opm-project.org/?page_id=239>`_) from the master branches with mpi support by running
+in the terminal the following lines (which in turn should build flow in the folder ./build/opm-simulators/bin/flow.): 
+
+.. code-block:: console
+
+    CURRENT_DIRECTORY="$PWD"
+
+    for repo in common grid models simulators
+    do
+        git clone https://github.com/OPM/opm-$repo.git
+    done
+
+    mkdir build
+
+    for repo in common grid models
+    do
+        mkdir build/opm-$repo
+        cd build/opm-$repo
+        cmake -DUSE_MPI=1 -DWITH_NDEBUG=1 -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="$CURRENT_DIRECTORY/build/opm-common;$CURRENT_DIRECTORY/build/opm-grid" $CURRENT_DIRECTORY/opm-$repo
+        make -j5 opm$repo
+        cd ../..
+    done    
+
+    mkdir build/opm-simulators
+    cd build/opm-simulators
+    cmake -DUSE_MPI=1 -DWITH_NDEBUG=1 -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="$CURRENT_DIRECTORY/build/opm-common;$CURRENT_DIRECTORY/build/opm-grid;$CURRENT_DIRECTORY/build/opm-models" $CURRENT_DIRECTORY/opm-simulators
+    make -j5 flow
+    cd ../..
+
+
+.. tip::
+
+    You can create a .sh file (e.g., build_opm_mpi.sh), copy the previous lines, and run in the terminal **source build_opm_mpi.sh**
+
+Regarding the plotting functionality, it is possible to use the opm python package instead of resdata (e.g., it seems the opm Python package
+is faster than resdata to read large simulation files). To use opm, you first need to install it, by executing in the terminal **pip install opm**.  
+
+Source build in macOS
++++++++++++++++++++++
+For macOS, there are no available binary packages, so OPM Flow needs to be built from source, in addition to the dune libraries and the opm Python
+package (see the `prerequisites <https://opm-project.org/?page_id=239>`_, which can be installed using macports or brew). This can be achieved by the following lines:
+
+.. code-block:: console
+
+    CURRENT_DIRECTORY="$PWD"
+
+    for module in common geometry grid istl
+    do   git clone https://gitlab.dune-project.org/core/dune-$module.git --branch v2.9.1
+    done
+    for module in common geometry grid istl
+    do   ./dune-common/bin/dunecontrol --only=dune-$module cmake -DCMAKE_DISABLE_FIND_PACKAGE_MPI=1
+         ./dune-common/bin/dunecontrol --only=dune-$module make -j5
+    done
+
+    for repo in common grid models simulators
+    do
+        git clone https://github.com/OPM/opm-$repo.git
+    done
+
+    source vpyopmnearwell/bin/activate
+
+    mkdir build
+
+    for repo in common grid models
+    do
+        mkdir build/opm-$repo
+        cd build/opm-$repo
+        cmake -DPYTHON_EXECUTABLE=$(which python) -DWITH_NDEBUG=1 -DUSE_MPI=0 -DOPM_ENABLE_PYTHON=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="$CURRENT_DIRECTORY/dune-common/build-cmake;$CURRENT_DIRECTORY/dune-grid/build-cmake;$CURRENT_DIRECTORY/dune-geometry/build-cmake;$CURRENT_DIRECTORY/dune-istl/build-cmake;$CURRENT_DIRECTORY/build/opm-common;$CURRENT_DIRECTORY/build/opm-grid" $CURRENT_DIRECTORY/opm-$repo
+        make -j5 opm$repo
+        cd ../..
+    done    
+
+    mkdir build/opm-simulators
+    cd build/opm-simulators
+    cmake -DUSE_MPI=0 -DWITH_NDEBUG=1 -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="$CURRENT_DIRECTORY/dune-common/build-cmake;$CURRENT_DIRECTORY/dune-grid/build-cmake;$CURRENT_DIRECTORY/dune-geometry/build-cmake;$CURRENT_DIRECTORY/dune-istl/build-cmake;$CURRENT_DIRECTORY/build/opm-common;$CURRENT_DIRECTORY/build/opm-grid;$CURRENT_DIRECTORY/build/opm-models" $CURRENT_DIRECTORY/opm-simulators
+    make -j5 flow
+    cd ../..
+
+    echo "export PYTHONPATH=\$PYTHONPATH:$CURRENT_DIRECTORY/build/opm-common/build/python" >> $CURRENT_DIRECTORY/vpyopmnearwell/bin/activate
+
+
+This builds OPM Flow as well as the opm Python package, and it exports the required PYTHONPATH. Then after execution, deactivate and activate the Python virtual environment.

docs/_sources/introduction.rst.txt

@@ -7,7 +7,7 @@ Introduction
 
 This documentation describes the content of the **pyopmnearwell** package.
 The numerical studies are performed using the 
-`Flow <https://opm-project.org/?page_id=19>`_ simulator.
+`OPM Flow <https://opm-project.org/?page_id=19>`_ simulator.
 
 Concept
 -------
@@ -44,14 +44,6 @@ where
 - \-s, \-scale: Scale for the x axis in the figures: 'normal' or 'log' ('normal' by default).
 - \-m, \-model: Simulated model (5th row in the configuration file). This is used for the plotting compare method (it gets overwritten by the configuration file) ('co2store' by default).
 
-Installation
-------------
-
-See the `Github page <https://github.com/cssr-tools/pyopmnearwell>`_.
-
-.. tip::
-    Check the `CI.yml <https://github.com/cssr-tools/pyopmnearwell/blob/main/.github/workflows/CI.yml>`_ file.
-
 .. warning::
     The H2CH4 template in the h2store model folder is under development and it is based on an input deck available in 
     `opm-tests <https://github.com/OPM/opm-tests/blob/master/diffusion/BO_DIFFUSE_CASE1.DATA>`_. In addition, the templates 

docs/_sources/pyopmnearwell.ml.analysis.rst.txt

@@ -0,0 +1,8 @@
+pyopmnearwell.ml.analysis module
+================================
+
+.. automodule:: pyopmnearwell.ml.analysis
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:

docs/_sources/pyopmnearwell.ml.resdata_dataset.rst.txt

@@ -0,0 +1,8 @@
+pyopmnearwell.ml.resdata\_dataset module
+========================================
+
+.. automodule:: pyopmnearwell.ml.resdata_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:

docs/_sources/pyopmnearwell.ml.rst.txt

@@ -7,12 +7,15 @@ Submodules
 .. toctree::
    :maxdepth: 4
 
-   pyopmnearwell.ml.data
+   pyopmnearwell.ml.analysis
    pyopmnearwell.ml.ensemble
    pyopmnearwell.ml.integration
    pyopmnearwell.ml.kerasify
    pyopmnearwell.ml.nn
+   pyopmnearwell.ml.resdata_dataset
    pyopmnearwell.ml.scaler_layers
+   pyopmnearwell.ml.upscale
+   pyopmnearwell.ml.utils
 
 Module contents
 ---------------

docs/_sources/pyopmnearwell.ml.upscale.rst.txt

@@ -0,0 +1,8 @@
+pyopmnearwell.ml.upscale module
+===============================
+
+.. automodule:: pyopmnearwell.ml.upscale
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members: