https://github.com/patnr/HistoryMatching

Notebooks on production optimisation and history matching
https://github.com/patnr/HistoryMatching

data-assimilation history-matching optimization pde-constrained-optimization reservoir-characterization

Last synced: about 2 months ago
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Notebooks on production optimisation and history matching

• Host: GitHub
• URL: https://github.com/patnr/HistoryMatching
• Owner: patnr
• License: mit
• Created: 2020-06-14T19:02:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-02-12T10:12:50.000Z (7 months ago)
• Last Synced: 2024-05-16T10:08:20.606Z (4 months ago)
• Topics: data-assimilation, history-matching, optimization, pde-constrained-optimization, reservoir-characterization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.7 MB
• Stars: 20
• Watchers: 2
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# History matching tutorial

![Screenshots](./screenshots.png)

## Run in the cloud (no installation required)

- on Colab (requires Google login):

  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/patnr/HistoryMatching)

- on a NORCE server (not generally available):

  [![JupyterHub](https://img.shields.io/static/v1?label=JupyterHub&message=by%20DIGIRES&logo=jupyter&color=blue)](https://jupyterhub.fredagsmorgen.no/hub?next=%2Fuser-redirect%2Fgit-pull?repo%3Dhttps%253A%252F%252Fgithub.com%252Fpatricknraanes%252FHistoryMatching%26branch%3Dmaster)

## OR: install

Use this option for development, or if you simply want faster computations

(your typical laptop is 10x faster than Google's free offering).

#### Prerequisite: Python>=3.10

If you're an expert, setup a python environment however you like.

Otherwise:

Install [Anaconda](https://www.anaconda.com/download), then

open the [Anaconda terminal](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html#starting-conda)

and run the following commands:

```bash

conda create --yes --name my-env python=3.10

conda activate my-env

python --version

```

Ensure the printed version is 3.10 or higher.  

*Keep using the same terminal for the commands below.*

#### Install

- `git clone` this repository (see the green button up top).  

  *You could instead download & unzip, but then you will

  have to manually download any later updates.*

- Move the resulting folder wherever you like

- `cd` into the folder

- Install requirements:  

  `pip install -r [path/to/]requirements-dev.txt`

#### Launch

- Launch the "notebook server" by executing:  

  `jupyter notebook`  

  This will open up a page in your web browser that is a file navigator.  

- Click on `HistoryMatch.ipynb`.

## Developer guide

I prefer to develop mostly in the format of standard python script,

which is why each notebook corresponds to a `.py` file synced via [jupytext](https://jupytext.readthedocs.io/en/latest/).

The synchronization is done whenever the notebook is saved.

Also, if you run `pre-commit install`,

then the notebooks will get synced with the `.py` files before committing.

Linting (which is, as of now, just a suggestion) can be run with

`ruff check --output-format=grouped`.

## Contributors

This work has been developed by *Patrick N. Raanes*, researcher at *NORCE*.

The project has been funded by *DIGIRES*,

a project sponsored by industry partners

and the *PETROMAKS2* programme of the *Research Council of Norway*.