| Name | pta-learn JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | None |
| Summary | A Python library for automated feature extraction and pattern recognition in Pressure Transient Analysis (PTA) workflows. |
| upload_time | 2025-02-14 07:13:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 [AutoWell](https://www.norceresearch.no/en/projects/autowell-automated-well-monitoring-and-control) project team including [Vitaly Starikov](https://www.linkedin.com/in/vitalystarikov/).
Permission is hereby granted, free of charge, to any person obtaining a copy
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| keywords |
feature-extraction
pattern-recognition
pressure-transient-analysis
well-testing
|
| VCS |
 |
| bugtrack_url |
|
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# pta-learn
A Python library for automated feature extraction and pattern recognition for Pressure Transient Analysis (PTA) workflows. The library provides tools to detect flow regime features and recognize stable patterns in time-lapse pressure transient responses. The library is 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).
Usage examples provided in:
<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>
## Installation
Install the package using pip:
```bash
pip install pta-learn
```
## Usage
### Feature Extraction
- Automated flow regime feature detection
- Parameter optimization
- Visualization tools
- Multiple export formats (Excel, CSV)
The `PTAClassifier` class provides automated flow regime detection for single pressure transient analysis:
```python
from pta_learn import PTAClassifier
import numpy as np
# Prepare your data as a 2D numpy array with columns: [time, pressure, pressure_derivative]
data = np.array([
[time_values],
[pressure_values],
[pressure_derivative_values]
]).T
# Initialize classifier
clf = PTAClassifier()
# Option 1: Fit and predict separately
clf.fit(data)
output, distance = clf.predict()
# Option 2: Fit and predict in one step
output, distance = clf.fit_predict(data)
# Option 3: Optimize parameters and predict
output, distance, optimization_result = clf.predict_optimize()
# Plot results
fig = clf.plot(industry_chart=True)
# Export results
clf.to_excel("results") # or
clf.to_csv("results")
```
### Pattern Recognition
- Multi-transient analysis
- Stable pattern recognition
- Confidence estimation
- Result visualization
The `PatternRecognition` class enables multi-transient analysis to identify stable patterns:
```python
from pta_learn import PatternRecognition
import pandas as pd
# Prepare list of dataframes, each containing [time, pressure, pressure_derivative]
transient1 = pd.DataFrame({
'time': time_values1,
'pressure': pressure_values1,
'pressure_derivative': pressure_derivative_values1
})
transient2 = pd.DataFrame({...})
data = [transient1, transient2]
# Initialize pattern recognition
pr = PatternRecognition()
# Fit data
pr.fit(data)
# Detect features
pr.detect_features()
# Get stable pattern
fig = pr.get_stable_pattern()
# Export results
pr.to_excel("pattern_results")
```
## 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}
}
```
## Acknowledgements
This research code was developed within the AutoWell research and development project funded by the Research Council of Norway and the industry partners including ConocoPhillips Skandinavia, Sumitomo Corporation Europe Norway Branch, Harbour Energy and Aker BP (grant no. 326580, PETROMAKS2 programme)
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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"description": "# pta-learn\n\nA Python library for automated feature extraction and pattern recognition for Pressure Transient Analysis (PTA) workflows. The library provides tools to detect flow regime features and recognize stable patterns in time-lapse pressure transient responses. The library is 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).\n\nUsage examples provided in:\n\n<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>\n<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>\n\n\n## Installation\n\nInstall the package using pip:\n\n```bash\npip install pta-learn\n```\n\n\n## Usage\n\n### Feature Extraction\n\n- Automated flow regime feature detection\n- Parameter optimization\n- Visualization tools\n- Multiple export formats (Excel, CSV)\n\n\nThe `PTAClassifier` class provides automated flow regime detection for single pressure transient analysis:\n\n```python\nfrom pta_learn import PTAClassifier\nimport numpy as np\n\n# Prepare your data as a 2D numpy array with columns: [time, pressure, pressure_derivative]\ndata = np.array([\n [time_values],\n [pressure_values],\n [pressure_derivative_values]\n]).T\n\n# Initialize classifier\nclf = PTAClassifier()\n\n# Option 1: Fit and predict separately\nclf.fit(data)\noutput, distance = clf.predict()\n\n# Option 2: Fit and predict in one step\noutput, distance = clf.fit_predict(data)\n\n# Option 3: Optimize parameters and predict\noutput, distance, optimization_result = clf.predict_optimize()\n\n# Plot results\nfig = clf.plot(industry_chart=True)\n\n# Export results\nclf.to_excel(\"results\") # or\nclf.to_csv(\"results\")\n```\n\n### Pattern Recognition\n- Multi-transient analysis\n- Stable pattern recognition\n- Confidence estimation\n- Result visualization\n\nThe `PatternRecognition` class enables multi-transient analysis to identify stable patterns:\n\n```python\nfrom pta_learn import PatternRecognition\nimport pandas as pd\n\n# Prepare list of dataframes, each containing [time, pressure, pressure_derivative]\ntransient1 = pd.DataFrame({\n 'time': time_values1,\n 'pressure': pressure_values1,\n 'pressure_derivative': pressure_derivative_values1\n})\ntransient2 = pd.DataFrame({...})\ndata = [transient1, transient2]\n\n# Initialize pattern recognition\npr = PatternRecognition()\n\n# Fit data\npr.fit(data)\n\n# Detect features\npr.detect_features()\n\n# Get stable pattern\nfig = pr.get_stable_pattern()\n\n# Export results\npr.to_excel(\"pattern_results\")\n```\n\n## Citation\n\nIf you use this library in your research, please cite:\n\n```\n@article{starikov2024feature,\n title={Feature extraction and pattern recognition in time-lapse pressure transient responses},\n author={Starikov, V. and Shchipanov, A. and Demyanov, V. and Muradov, K.},\n journal={Geoenergy Science and Engineering},\n volume={242},\n pages={213160},\n year={2024},\n publisher={Elsevier},\n doi={10.1016/j.geoen.2024.213160},\n url={https://www.sciencedirect.com/science/article/pii/S294989102400530X}\n}\n\n@conference{starikov2023unsupervised,\n title={Unsupervised Classification of Flow Regime Features in Pressure Transient Responses},\n author={Starikov, V. and Demyanov, V. and Muradov, K. and Shchipanov, A.},\n booktitle={Fifth EAGE Conference on Petroleum Geostatistics},\n year={2023},\n month={Nov},\n pages={1-5},\n publisher={European Association of Geoscientists & Engineers},\n doi={10.3997/2214-4609.202335019}\n}\n```\n## Acknowledgements\n\nThis research code was developed within the AutoWell research and development project funded by the Research Council of Norway and the industry partners including ConocoPhillips Skandinavia, Sumitomo Corporation Europe Norway Branch, Harbour Energy and Aker BP (grant no. 326580, PETROMAKS2 programme)\u200b\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n",
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