Metadata-Version: 2.4
Name: pta-learn
Version: 1.0.6
Summary: A Python library for automated Pressure Transient Analysis (PTA) workflows. It provides tools for PTA flow regime feature extraction, time-lapse pattern recognition, pressure transient identification, and Bourdet derivative calculation.
Project-URL: Homepage, https://github.com/vitaliistarr/pta-learn
Project-URL: Repository, https://github.com/vitaliistarr/pta-learn.git
Project-URL: Bug Tracker, https://github.com/vitaliistarr/pta-learn/issues
Author-email: "V. Starikov" <starikovv98@gmail.com>, "B. Cui" <boyu.cui@qq.com>
License-Expression: Apache-2.0
License-File: LICENSE
License-File: NOTICE
Keywords: Bourdet-derivative,feature-extraction,loglog-plot,pattern-recognition,pressure-transient-analysis,transient-identification,well-testing
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.12
Requires-Dist: kneebow==1.0.1
Requires-Dist: matplotlib==3.10.1
Requires-Dist: numba>=0.61.0
Requires-Dist: numpy==2.1.3
Requires-Dist: pandas==2.2.3
Requires-Dist: plotly>=5.24.1
Requires-Dist: scikit-learn==1.6.1
Requires-Dist: scikit-optimize==0.10.2
Requires-Dist: scipy==1.15.0
Provides-Extra: dev
Requires-Dist: black>=22.0; extra == 'dev'
Requires-Dist: isort>=5.0; extra == 'dev'
Requires-Dist: pytest-cov>=2.0; extra == 'dev'
Requires-Dist: pytest>=6.0; extra == 'dev'
Requires-Dist: requests>=2.31.0; extra == 'dev'
Description-Content-Type: text/markdown

# pta-learn

A Python library for automated Pressure Transient Analysis (PTA) workflows. The library provides tools to identify shut-in and flowing transients, detect PTA flow regime features and recognize stable patterns in time-lapse pressure transient responses. 
Feature extraction and pattern recognition modules are based on the methodology described in the peer-reviewed paper: [Feature extraction and pattern recognition in time-lapse pressure transient responses](https://doi.org/10.1016/j.geoen.2024.213160).
Shut-in pressure transient identification module is implemented using the methodology detailed in the conference paper: [TPMR - A Novel Method for Automated Identification of Well Pressure Transients](https://doi.org/10.3997/2214-4609.202310910). 
Similarly, the flowing transient identification module employs the approach described in the conference paper: [LMIR - A New Method for Automated Identification of Multi-Rate Pressure Transients](https://doi.org/10.3997/2214-4609.202410313).

## Installation

Install the package using pip:

```bash
pip install pta-learn
```


## Usage

### Transient Identification
<a href="https://colab.research.google.com/drive/1SsXoKafnEJWafGUk8FHa_d7oF6ODljHI?usp=sharing"> Integrated Transient Identification Workflow example <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a> <br>
<a href="https://colab.research.google.com/drive/1z9B7RzGkWfQEpRWUygTIJvKtjnSeexE4?usp=sharing"> Shutin Transient Identification by TPMR method example <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a> <br>
<a href="https://colab.research.google.com/drive/1WFA9hKydEoxk1Z60_NFCQQJwhT9QwMEw?usp=sharing"> Flowing Transient Identification by LMIR method <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a> <br>

### Bourdet Derivative and Loglog Plot Calculation
<a href="https://colab.research.google.com/drive/1lVzSIklC-51Nzqehp4lOFctKCjhEbX3Z?usp=sharing"> Loglog family ploting Workflow example <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a>

### Pattern Recognition in Time-lapse Pressure Transient Responses
<a href="https://colab.research.google.com/drive/1ioJiNM5xpNyP1NoVpBrQp1qr1u94Knlz?usp=sharing"> PTA flow regime feature extraction example <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a> <br>
<a href="https://colab.research.google.com/drive/1_ASQ8nmRewhCZmNSMPcs3WBmBFFGiSs6?usp=sharing"> Time-lapse PTA pattern recognition example <img src="https://colab.research.google.com/assets/colab-badge.svg" height=16px></a> <br>


## Citation

If you use this library in your research, please cite:

```
@article{starikov2024feature,
  title={Feature extraction and pattern recognition in time-lapse pressure transient responses},
  author={Starikov, V. and Shchipanov, A. and Demyanov, V. and Muradov, K.},
  journal={Geoenergy Science and Engineering},
  volume={242},
  pages={213160},
  year={2024},
  publisher={Elsevier},
  doi={10.1016/j.geoen.2024.213160},
  url={https://www.sciencedirect.com/science/article/pii/S294989102400530X}
}

@conference{starikov2023unsupervised,
  title={Unsupervised Classification of Flow Regime Features in Pressure Transient Responses},
  author={Starikov, V. and Demyanov, V. and Muradov, K. and Shchipanov, A.},
  booktitle={Fifth EAGE Conference on Petroleum Geostatistics},
  year={2023},
  month={Nov},
  pages={1-5},
  publisher={European Association of Geoscientists & Engineers},
  doi={10.3997/2214-4609.202335019}
}

@conference{Boyu2023tpmr,
  title={TPMR - A Novel Method for Automated Identification of Well Pressure Transients},
  author={Cui,B. and Zhang,N. and Shchipanov,A. and Rong,C. and Demyanov,V.},
  booktitle={84th EAGE Annual Conference & Exhibition},
  year={2023},
  month={Jun},
  pages={1-5},
  publisher={European Association of Geoscientists & Engineers},
  doi={https://doi.org/10.3997/2214-4609.202310910}
}

@conference{Boyu2024lmir,
  title={LMIR - A New Method for Automated Identification of Multi-Rate Pressure Transients},
  author={Cui,B. and Shchipanov,A. and Zhang,N. and Demyanov,V. and Rong,C.},
  booktitle={85th EAGE Annual Conference & Exhibition},
  year={2024},
  month={Jun},
  pages={1-5},
  publisher={European Association of Geoscientists & Engineers},
  doi={https://doi.org/10.3997/2214-4609.202410313}
}



```
## Acknowledgements

This research code was developed within the following projects:

- AutoWell research and development project funded by the Research Council of Norway and the industry partners including
ConocoPhillips Skandinavia AS, Sumitomo Corporation Europe Norway Branch, Harbour Energy Norge AS and Aker BP ASA
(grant no. 326580, PETROMAKS2 programme).
- AutoWell Phase 2, a joint industry research and development project funded by ConocoPhillips Skandinavia AS,
Aker BP ASA, Harbour Energy Norge AS and TotalEnergies EP Norge AS.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

