Skip to main content

Support the intelligent fracturing process.

Project description

📦 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

pip install autofracture

To Do

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

Pull requests are encouraged!

More Resources

License

MIT License

Updata log

  • '0.0.1'-'0.0.8' test release
  • '0.0.1' first release

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autofracture-0.1.1.tar.gz (50.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

autofracture-0.1.1-py3-none-any.whl (52.6 kB view details)

Uploaded Python 3

File details

Details for the file autofracture-0.1.1.tar.gz.

File metadata

  • Download URL: autofracture-0.1.1.tar.gz
  • Upload date:
  • Size: 50.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.17

File hashes

Hashes for autofracture-0.1.1.tar.gz
Algorithm Hash digest
SHA256 f62693fd8a9cc296888d82632f920fdc1a0f7fe1603d65bef4f0ea290d99f492
MD5 b32c717a5a6c633b5d62cfd973cc2768
BLAKE2b-256 52109abcb79462ea6862b1b61231287983285063805d1cafe9d9010302e8bf77

See more details on using hashes here.

File details

Details for the file autofracture-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: autofracture-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 52.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.17

File hashes

Hashes for autofracture-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 0080879519c14d21f82393423e25de48e8c2077ab5f6f95202d9a4ae038fa5f3
MD5 1038e7ab66765b223f3f2aee1448b8f2
BLAKE2b-256 fabe0fd5d2be914426071cfba0401e5ac1a2e0a1ff0c26d1195a6c227516fa0e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page