Metadata-Version: 2.1
Name: autopetroleum
Version: 0.0.1
Summary: Support the intelligent fracturing process.
Home-page: https://github.com/MoonCapture/AutoFracture
Author: mzc
Author-email: mzc1226@126.com
License: MIT License
Platform: all
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: scipy
Requires-Dist: autogluon


📦 AutoFracture
===============

In the field of hydraulic fracturing, automatic machine learning can help in handling and analyzing large amounts of data, improving the accuracy of predicting hydraulic fracturing results, and optimizing operational parameters. Here are some aspects where automatic machine learning can play a role in the field of hydraulic fracturing:

1. Data analysis and feature engineering: Automatic machine learning algorithms can assist in analyzing various data generated during the hydraulic fracturing process, such as geological, seismic, fluid mechanics data, automatically generating features, and reducing data dimensions.
2. Predicting hydraulic fracturing outcomes: Using machine learning algorithms, it is possible to predict the outcomes of hydraulic fracturing, including parameters such as rock fracturing patterns, porosity, etc., helping engineers make more accurate decisions.
3. Optimizing parameter configurations: Through automated machine learning algorithms, it is possible to optimize the configuration of hydraulic fracturing parameters, such as the ratio of fracturing fluid, injection speed, injection volume, etc., to achieve more efficient and cost-effective hydraulic fracturing operations.
4. Real-time monitoring and adjustments: By combining sensors and automated machine learning algorithms, it is possible to monitor changes in parameters during the hydraulic fracturing process in real-time, and make timely adjustments to prevent unforeseen incidents, improving efficiency and safety of hydraulic fracturing.

In conclusion, automatic machine learning has significant potential applications in the field of hydraulic fracturing, assisting in optimizing hydraulic fracturing operations to enhance efficiency, accuracy, and cost-effectiveness.

Installation
------------

```bash
pip install autofracture
```

To Do
-----

- Tests via `$ setup.py test` (if it's concise).

Pull requests are encouraged!

More Resources
--------------

- [What is setup.py?] on Stack Overflow
- [Official Python Packaging User Guide](https://packaging.python.org)
- [The Hitchhiker's Guide to Packaging]
- [Cookiecutter template for a Python package]

License
-------

MIT License

## Updata log

* 
* `'0.0.1'-'0.0.8'`      test release
* `'0.0.1'`                 first release
