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. 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. 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.

Installation

Install the package using pip:

pip install pta-learn

Usage

Transient Identification

Integrated Transient Identification Workflow example
Shutin Transient Identification by TPMR method example
Flowing Transient Identification by LMIR method

Bourdet Derivative and Loglog Plot Calculation

Loglog family ploting Workflow example

Pattern Recognition in Time-lapse Pressure Transient Responses

PTA flow regime feature extraction example
Time-lapse PTA pattern recognition example

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.