A Python package for pore pressure prediction using well log data and seismic velocity data.
Cite as: Yu, (2018). PyGeoPressure: Geopressure Prediction in Python. Journal of Open Source Software, 3(30), 992, https://doi.org/10.21105/joss.00992
- Overburden (or Lithostatic) Pressure Calculation
- Eaton's method and Parameter Optimization
- Bowers' method and Parameter Optimization
- Multivariate method and Parameter Optimization
pyGeoPressure is on
pip install pygeopressure
Pore Pressure Prediction using well log data
import pygeopressure as ppp survey = ppp.Survey("CUG") well = survey.wells['CUG1'] a, b = ppp.optimize_nct(well.get_log("Velocity"), well.params['horizon']["T16"], well.params['horizon']["T20"]) n = ppp.optimize_eaton(well, "Velocity", "Overburden_Pressure", a, b) pres_eaton_log = well.eaton(np.array(well.get_log("Velocity").data), n) fig, ax = plt.subplots() ax.invert_yaxis() pres_eaton_log.plot(ax, color='blue') well.get_log("Overburden_Pressure").plot(ax, 'g') ax.plot(well.hydrostatic, well.depth, 'g', linestyle='--') well.plot_horizons(ax)
Read the documentaion for detailed explanations, tutorials and references: https://pygeopressure.readthedocs.io/en/latest/
If you find a bug, please report it at Github Issues by opening a new issue with
If you have new ideas or need new features, you can request them by opening a new issue at Github Issues with
enhancement label. We will see if we can work on it together.
Submit Pull Requests
If you would like to help fix known bugs, please submit a PR. (See The beginner's guide to contributing to a GitHub project, if you are new to Github).
Before creating a pull request, please try to make sure the tests pass and use numpy-style docstrings. (Please see the documentation on setting up the development environment https://pygeopressure.readthedocs.io/en/latest/install.html)
If you have any questions, please open an issue at Github Issues with
question label. Tell us about your question, we will provide assistance. And maybe we could add it to the documentation.
The project is licensed under the MIT license, see the file LICENSE for details.