Merge pull request #24 from daavid00/dev

Python 3.14, optional tensorflow, updating docs, and release 2025.10

Author: daavid00

.github/workflows/CI.yml

@@ -41,6 +41,7 @@ jobs:
       run: |
         pip install --upgrade pip setuptools wheel
         pip install -r dev-requirements.txt
+        pip install tensorflow
         
     - name: Install pyopmnearwell
       run: |
@@ -59,4 +60,4 @@ jobs:
     - name: Build documentation
       run: |
         pushd docs
-        make html
+        make html

.gitignore

@@ -187,4 +187,3 @@ playground/
 project/
 .vscode/
 test_outputs/
-

CONTRIBUTING.md

@@ -13,12 +13,14 @@ Contributions are more than welcome using the fork and pull request approach 
 1. Work on your own fork of the main repo
 1. In the main repo execute:
     1. **pip install -r dev-requirements.txt** (this installs the [_dev-requirements.txt_](https://github.com/cssr-tools/pyopmnearwell/blob/main/dev-requirements.txt))
+    1. **pip install tensorflow** (this install [_tensorflow_](https://www.tensorflow.org) for the machine learning near well funcitonality; tensorflow is not yet available for Python 3.14, then Python 3.12 or Python 3.13 are currently needed to run the tests)
     1. **black src/ tests/** (this formats the code)
     1. **pylint src/ tests/** (this analyses the code, and might rise issues that need to be fixed before the pull request)
     1. **mypy --ignore-missing-imports src/ tests/** (this is a static checker, and might rise issues that need to be fixed before the pull request)
     1. **pytest --cov=pyopmnearwell --cov-report term-missing tests/** (this runs locally the tests, and might rise issues that need to be fixed before the pull request; to save the files, add the flag **--basetemp=test_outputs**)
     1. **pushd docs & make html** (this generates the documentation, and might rise issues that need to be fixed before the pull request; if the build succeeds and if the contribution changes the documentation, then copy all content from the docs/_build/html/ folder and replace the files in the [_docs_](https://github.com/cssr-tools/pyopmnearwell/tree/main/docs) folder)
-    * Tip: See the [_CI.yml_](https://github.com/cssr-tools/pyopmnearwell/blob/main/.github/workflows/CI.yml) script and the [_Actions_](https://github.com/cssr-tools/pyopmnearwell/actions) for installation of pyopmnearwell, OPM Flow (binary packages), and dependencies, as well as the execution of the six previous steps in Ubuntu 24.10.
+    * Tip for Linux users: See the [_CI.yml_](https://github.com/cssr-tools/pyopmnearwell/blob/main/.github/workflows/CI.yml) script and the [_Actions_](https://github.com/cssr-tools/pyopmnearwell/actions) for installation of pyopmnearwell, OPM Flow (binary packages), and dependencies, as well as the execution of the seven previous steps in Ubuntu 24.04 using Python 3.12.
+    * Tip for macOS users: See the [_ci_pycopm_macos_.yml_](https://github.com/daavid00/OPM-Flow_macOS/blob/main/.github/workflows/ci_pycopm_macos.yml) script and the [_OPM-Flow_macOS Actions_](https://github.com/cssr-tools/pycopm/actions) for installation of pycopm (a related tool to pyopmnearwell), OPM Flow (source build), and dependencies in macOS 26 using Python 3.13. In addition, you need to add the directory containing the OPM Flow executable to your system's PATH environment variable (e.g., export PATH=$PATH:/Users/yourname/pyopmnearwell/build/opm-simulators/bin).
 1. Squash your commits into a single commit (see this [_nice tutorial_](https://gist.github.com/lpranam/4ae996b0a4bc37448dc80356efbca7fa) if you are not familiar with this)
 1. Push your commit and make a pull request
 1. The maintainers will review the pull request, and if the contribution is accepted, then it will be merge to the main repo 
@@ -32,4 +34,5 @@ Contributions are more than welcome using the fork and pull request approach 
 
 1. The preferred approach to seek support is to raise an Issue as described in the previous lines.
 1. We will try to answer as soon as possible, but also any user is more than welcome to answer.
-- An alternative approach is to send an email to any of the [_mantainers_](https://github.com/cssr-tools/pyopmnearwell/blob/main/pyproject.toml). 
+- An alternative approach is to send an email to any of the [_mantainers_](https://github.com/cssr-tools/pyopmnearwell/blob/main/pyproject.toml).
+ 

MANIFEST.in

@@ -8,4 +8,4 @@ include src/pyopmnearwell/templates/common/*
 include src/pyopmnearwell/templates/foam/*
 include src/pyopmnearwell/templates/h2store/*
 include src/pyopmnearwell/templates/saltprec/*
-include src/pyopmnearwell/utils/*
+include src/pyopmnearwell/utils/*

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-3.12%20to%203.13-blue.svg"></a>
+<a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.12%20to%203.14-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)
@@ -12,23 +12,23 @@ Simplified and flexible testing framework for near-well simulations via a config
 
 ## Installation
 You will first need to install
-* OPM Flow (https://opm-project.org, Release 2025.04 or current master branches)
+* OPM Flow (https://opm-project.org, Release 2025.10 or current master branches)
 
 To install the _pyopmnearwell_ executable from the development version: 
 
 ```bash
 pip install git+https://github.com/cssr-tools/pyopmnearwell.git
 ```
 
-If you are interested in a specific version (e.g., v2025.04) or in modifying the source code, then you can clone the repository and install the Python requirements in a virtual environment with the following commands:
+If you are interested in a specific version (e.g., v2025.10) or 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
 git clone https://github.com/cssr-tools/pyopmnearwell.git
 # Get inside the folder
 cd pyopmnearwell
-# For a specific version (e.g., v2025.04), or skip this step (i.e., edge version)
-git checkout v2025.04
+# For a specific version (e.g., v2025.10), or skip this step (i.e., edge version)
+git checkout v2025.10
 # Create virtual environment
 python3 -m venv vpyopmnearwell
 # Activate virtual environment
@@ -41,7 +41,7 @@ pip install -e .
 pip install -r dev-requirements.txt
 ``` 
 
-See the [_installation_](https://cssr-tools.github.io/pyopmnearwell/installation.html) for further details on building OPM Flow from the master branches in Linux, Windows, and macOS, as well as the opm and tensorflow Python packages and LaTeX dependencies.
+See the [_installation_](https://cssr-tools.github.io/pyopmnearwell/installation.html) for further details on building OPM Flow from the master branches in Linux, Windows (via [_WSL_](https://learn.microsoft.com/en-us/windows/wsl/)), and macOS, as well as the [_tensorflow_](https://www.tensorflow.org) Python package for the machine larning near well functionality.
 
 ## Running pyopmnearwell
 You can run _pyopmnearwell_ as a single command line:
@@ -62,10 +62,10 @@ See the [_examples_](https://cssr-tools.github.io/pyopmnearwell/examples.html) i
 ## Publications
 The following is a list of manuscripts in which _pyopmnearwell_ is used:
 
-1. Landa-Marbán, D., Zamani, N., Sandve, T.H., Gasda, S.E., 2024. Impact of Intermittency on Salt Precipitation During CO2 Injection, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. doi: 10.2118/218477-MS.   
-1. von Schultzendorff, P., Sandve, T.H., Kane, B., Landa-Marbán, D., Both, J.W., Nordbotten, J.M., 2024. A Machine-Learned Near-Well Model in OPM Flow, presented at ECMOR 2024, European Association of Geoscientists & Engineers, Sep. 2024, pp. 1–23. doi: 10.3997/2214-4609.202437033.
-1. Lliguizaca, J.R., Landa-Marbán, D., Gasda, S.E., Sandve, T.H., Alcorn, Z.P., 2024. Data-Driven Predictions of CO2 EOR Numerical Studies Using Machine Learning in an Open-Source Framework, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. doi: 10.2118/218441-MS.
-1. Mushabe, R., Minougou, J.D., Landa-Marbán, D., Kane, B., Sandve, T.H., To appear. Predicting Ultimate Hydrogen Production and Residual Volume during Cyclic Underground Hydrogen Storage in Porous Media using Machine Learning.
+1. Landa-Marbán, D., Zamani, N., Sandve, T.H., Gasda, S.E., 2024. Impact of Intermittency on Salt Precipitation During CO2 Injection, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. https://doi.org/10.2118/218477-MS. 
+1. von Schultzendorff, P., Sandve, T.H., Kane, B., Landa-Marbán, D., Both, J.W., Nordbotten, J.M., 2024. A Machine-Learned Near-Well Model in OPM Flow, presented at ECMOR 2024, European Association of Geoscientists & Engineers, Sep. 2024, pp. 1–23. https://doi.org/10.3997/2214-4609.202437033.
+1. Lliguizaca, J.R., Landa-Marbán, D., Gasda, S.E., Sandve, T.H., Alcorn, Z.P., 2024. Data-Driven Predictions of CO2 EOR Numerical Studies Using Machine Learning in an Open-Source Framework, presented at SPE Norway Subsurface Conference, Bergen, Norway, April 2024. https://doi.org/10.2118/218441-MS.
+1. Mushabe, R., Minougou, J.D., Landa-Marbán, D., Kane, B., Sandve, T.H., 2025. Predicting Ultimate Hydrogen Production and Residual Volume during Cyclic Underground Hydrogen Storage in Porous Media using Machine Learning. ESAIM: Proceedings and Surveys, 81, pp. 145-167. https://doi.org/10.1051/proc/202581145.
 
 ## 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
-numpydoc
-mypy
-pylint
-pytest-cov
-sphinx
-sphinx-rtd-theme
+black<=25.9.0
+numpydoc<=1.9.0
+mypy<=1.18.2
+pylint<=4.0.2
+pytest-cov<=7.0.0
+sphinx<=8.2.3
+sphinx-rtd-theme<=3.0.2

docs/_images/pofff.png

@@ -10,4 +10,4 @@ The **pyopmnearwell** package is being funded by the `HPC Simulation Software fo
 
 Contributions are more than welcome using the fork and pull request approach.
 
-For a new feature, please request this raising an issue.
+For a new feature, please request this raising an issue.

docs/_sources/about.rst.txt

@@ -11,4 +11,4 @@ constains scripts used in `this repository <https://github.com/cssr-tools/ML_nea
 
     Files in the pyopmnearwell tool.
 
-.. include:: modules.rst
+.. include:: modules.rst

docs/_sources/api.rst.txt

@@ -171,4 +171,5 @@ Additional parameters
 
 For different models than the co2store, new variables are used from the ones explained here. Then, in each of the 
 configuration files, a short description of the variable is added, e.g., for the saltprec model, then the poro-perm 
-relationship and the parameters per different facies can be set, see `saltprec.toml <https://github.com/cssr-tools/pyopmnearwell/blob/main/examples/saltprec.toml>`_.
+relationship and the parameters per different facies can be set, see `saltprec.toml <https://github.com/cssr-tools/pyopmnearwell/blob/main/examples/saltprec.toml>`_, and for
+biofilm effects in hydrogen storage, then the parameters for the biofilm can be set, see `h2biofilm.toml <https://github.com/cssr-tools/pyopmnearwell/blob/main/examples/h2biofilm.toml>`_.